Characteristics of rheumatoid arthritis clinical trials over past decade 2013-2023: current landscape and opportunities for improvement

Characteristics of rheumatoid arthritis clinical trials over past decade 2013-2023: current landscape and opportunities for improvement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characteristics of rheumatoid arthritis clinical trials over past decade 2013-2023: current landscape and opportunities for improvement Wenliang Dong, Gengchen Wang, Xiaowen Hu, Xue Wang, Yi Fang, Luwen Shi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4674898/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There is a disconnection between the continued pressing clinical demand for rheumatoid arthritis (RA) treatments and the saturation of the current therapeutic markets. The design of rheumatoid arthritis trials might represent one of significant barrier to advancing therapeutic progress. A comprehensive review was performed to evaluate the characteristics of RA trials registered in ClinicalTrials.gov from 2013 to 2023. Methods The ClinicalTrials.gov database was searched for trials focused on the RA interventional trials from 2013 to 2023. Interventional drug or biological trials were included. Key characteristics of RA trials were summarized and target population, control groups selection, and clinical endpoints were evaluated. Results Between January 2013 and December 2023, 425 RA trials were included. Decreased trial numbers, excessive industry sponsorship, and delayed published results were found. For target population, 28% clinical trials didn’t define distinct RA patients, and 38% of the trials included population with no upper age limit. For control groups, only 36% trials had head-to-head comparisons, 50% were placebo-controlled, where half of placebo-controlled trials were with special design (add-on, early escape, double dummy), and half without any design. For clinical endpoints, ACR20 (24%) and DAS28 (21%) were the most commonly used outcomes, with declining ACR20 and ascending DAS28. Only 7% trials adherence to “treat-to-target” strategy, but the most commonly used outcome measures not aligned with guideline-recommended. Conclusions Our study contributes to a nuanced comprehension of the current landscape of RA trials and offers valuable insights for future improvement. This included the necessity of stratifying the target population based on disease activity or treatment history to achieve precision in treatment; considerations of more stringent or sensitive clinical endpoints to provide better discriminatory power; addressing discrepancies between the endpoints selected for treat-to-target and those recommended by guidelines to choose optimal treatment strategy. Rheumatoid arthritis Clinical trial Characteristics Figures Figure 1 Figure 2 Background Rheumatoid arthritis (RA) is a common chronic inflammatory disease causing significant joint damage, substantial disability, morbidity, and premature mortality[ 1 ]. Over the past 25 years, a total of 15 biologic disease modifying antirheumatic drugs (bioDMARDs) and targeted synthetic disease modifying antirheumatic drugs (tsDMARDs) have been approved for RA treatment worldwide, including etanercept, infliximab, adalimumab, certolizumab pegol, golimumab, ozoralizumab, tocilizumab, sarilumab, anakinra, abatacept, rituximab, tofacitinib, baricitinib, upadacitinib, and filgotinib. As such, rheumatoid arthritis is set to one of dominant factors in the development of antibody and targeted drugs[ 2 ]. Since the approval of the first biologic drug, the management of RA has been revolutionized, leading to significantly decreased levels of disease activity and better long-term outcomes[ 3 ]. Despite these advances, patients responding to drugs and achieving remission is still far from ideal. 40% patients could not respond to any RA drugs including bioDMARDs and tsDMARDs, and only 30% patients could achieve clinical remission[ 4 , 5 ]. Rheumatoid arthritis seems to face a paradox: on the one hand, there are many kinds of innovative drugs available for RA treatment, on the other hand, there are many clinical needs that have still been unaddressed. The disconnect between the still urgent clinical unmet needs and crowded therapies can be attributed to various factors, some of which are related to the lack of full understanding to pathogenetic mechanisms and clinical obstacles inherent to RA[ 6 ]. Nonetheless, the design of RA clinical trials was also an important barrier to progress. Primary issues about the RA trial design included the limitation of placebo use in RA clinical trials[ 7 ], characteristics gaps between patients enrolled in RCTs and real world[ 8 ], exclusion of older adults[ 9 ], positive study outcome associated with publication and timeliness of publication[ 10 ], and appropriate endpoints selection[ 11 ]. Mounting evidence pressures on drug manufacturers and regulations to differentiate RA products in congested therapeutic markets can necessitate adequately designing clinical trials with sufficient power to answer these relevant scientific questions. Here, we summarized the key characteristics of RA clinical trials listed on ClinicalTrials.gov over the past decade, including the studied medications, participants, controls, and endpoints, providing an up-to-date overview of the RA clinical trials landscape. Methods This retrospective observational study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. This is secondary research on existing public data that involves no human participants, individual data, or any specimens. Therefore, ethical approval and informed consent were not required according to Peking University Institutional Review Board regulations. Search Strategy We performed a systematical search on ClinicalTrials.gov ( https://clinicaltrials.gov/ ) for RA trials registered between January 01, 2013 and December 31, 2023. Trials were searched for based on “Rheumatoid Arthritis” condition and “Interventional Studies (Clinical Trials)” study type. Any trials with a listed status terminated, withdrawn, or suspended, and trials with interventions other than drug or biological were all excluded. Two researchers (G.C.W. and X.W.H.) independently screened and reviewed the database of all potential trials to be included in this study based on the inclusion and exclusion criteria. Data was independently extracted and disagreements were resolved through consensus. Outcome Reporting For each eligible trial, we recorded interventions, population characteristics, study characteristics, and endpoints and its evaluation time. For each medication, we identified the drug class, target, and detailed drug information, from YaoZhi database ( www.yaozh.com , one of the most authoritative platforms of drug information in China). We summarized the key characteristics of RA trials over the past decade. In addition, we classified trials into trial phases and trial characteristics based on their target population, control arms selection, and clinical endpoints. For target population, we focused on the levels of disease activity and treatment histories for RA patients, age limits and gender in the eligibility criteria. For control groups, we evaluated the active/placebo control and utilization and duration of placebo administration. For clinical endpoints, we assessed the variation of the endpoints used in RA trials supporting drug clinical efficacy evaluation. Statistical Analysis Descriptive statistics were calculated for trial key characteristics. Categorical variables were described as frequencies and percentages, whereas continuous ones as medians and interquartile ranges (IQRs). Analyses were performed using R version 4.3.1 ( https://www.r-project.org , USA). Results From January 2013 to December 2023, 425 trials with 103,431 participants were identified (Fig. 1 ). Although the number of RA clinical trials, particularly for bioDMARDs, has declined in the past decade, bioDMARDs continue to dominate in the RA field, accounting for 47% of all RA clinical trials (Figure S1 -S2). Among these bioDMARDs clinical trials, the primary focus has been on the advancement of monoclonal antibodies, followed by fusion proteins (Figure S2). Biosimilars clinical trials accounted for 28% of bioDMARDs, mainly adalimumab, followed by infliximab and rituximab (Figure S3). The target molecular for the new drug trials are highly focused in the past ten years —JAK, TNF, IL-6/IL-6R, BTK, CD20— accounting for 48% of all RA clinical trials (Table S1 ). Trial characteristics Key characteristics of the clinical trials were listed in Table 1 . The median (IQRs) study duration was 12 (4–13) months, and the median (IQRs) sample size was 120 (48–300) participants. Of the 231 completed clinical trials by 2021, 99 (43%) did not report any results (either publication results or the results demonstrated in ClinicalTrials.gov) at 2 years after completion, enrolled more than 10,000 participants. In general, the number of phase I, II, and III clinical trials is approximately equal, however, over the past decade, there has been a decline in the number of phase III clinical trials, while the number of phase I and II clinical trials has been on the rise (Figure S4). Industry was the most common sponsor (73%), followed by academia and government (21%), however, there has been a discernible decline in the quantity of industry-funded RA clinical trials over the preceding decade (Figure S5). Study populations Characteristics of included population were listed in Table 2 . While 121 trials (28%) assessed treatment efficacy for general RA populations and 68 trials (16%) for general active RA patients, 121 trials (28%) focused on RA patients with different disease activity and 77 trials (18%) were for RA patients with distinct treatment histories. Moderate-to-high disease activity (26%) was the most common subpopulation in RA clinical trials, with an increasing trend in past decade. A total of 199 trials (47%) had an upper age limit of 65 years or more and 161 trials (38%) without an upper age limit. Control groups Control groups in RA clinical trials over the past decade were depicted in Table 3 . Of all trials, 154 trials (36%) included active controls and 61 trials (14%) did not have control groups. In 210 trials (50%), investigating drugs were tested against placebo; 97 of these trials (46%) were placebo control with variants designs [81 trials (83%) with add-on design, 9 trials (9%) with early-escape design, 8 trials (8%) with double-dummy design] and 111 of these trials (53%) were placebo control without any design. Most of placebo-controlled trials without any design used placebo for no more than 12 weeks, however, 13 placebo-controlled trials without any design used placebo for more than 12 weeks, 5 trials of those included healthy volunteers. Clinical endpoints Primary endpoints to assess RA disease activity were listed at Table 4 . ACR20 and DAS28 were the most commonly used outcomes to evaluate RA response and disease activity state and change score, and DAS28 endpoints increased from 27–59% in 2013–2023, while ACR20 endpoints decreased from 49–12% (Fig. 2 ). Only 31 trials (7%) included remission or low disease activity to assess RA treatment efficacy; 26 of these trials (84%) were assessed by DAS28 and only 4 trials (6%) assessed by CDAI or SDAI. Discussion In this study, we summarized the characteristics of 425 RA clinical trials registered on ClinicalTrials.gov during the past decade. Reduced clinical trials, RA drug development dilemma Over the past decade, there has been a notable decrease in the number of RA clinical trials, with declining from 54 trials in 2013 to 32 trials in 2023, which partly reflected the challenge of RA drug development. Drug development of RA is challenged by the limited understanding of the pathogenetic mechanisms underpinning RA clinical heterogeneity and the specific pathways driving disease in different patients, along with the lack of biomarkers to predict response to RA drugs[ 12 – 14 ]. It helps that certain pioneering drug products for the crowded targets have proven transformative for patients, such as TNF inhibitors, JAK inhibitors, and IL-6/IL-6R agents, however, approximately 40% of patients exhibit no response to individual DMARDs, including bioDMARDs and tsDMARDs, which called ‘ceiling’ treatment response[ 15 ]. The declined number of industry-funded RA trials illustrated part of the dilemma in current RA clinical trials. The ‘ceiling’ treatment response, together with the high cost and complexity of RCTs, pharmaceutical companies have hesitated to allocate resources to extensive trials that may only yield response rates comparable to existing drugs, and in some instances they have de-prioritized developing RA drugs[ 16 ]. The advancement of novel drugs with diverse targets (BTK, PD-1, GM-CSF et al) or alternative technologies (cell therapy, bispecific antibody, ADC, nano antibody) were aspired to surmount the present impasse. Excessive industry sponsorship, delayed published results About three quarters of the RA trials were industry-funded. Previous studies concluded that industry support appeared to be associated with differences in trial design, results, and reporting[ 17 ]. For example, Industry sponsorship bias comparator selection, favoring products from the same company, and leads to more favorable efficacy results and conclusions than sponsorship by other sources[ 18 , 19 ]. Many clinical trials are often either never published or published after a significant delay. We also identified that only half of RA trials yielded results available for at least 2 years after completion during past decade. Although we did not explore the publication rate and time to publication, khan’s study [ 10 ] demonstrated that the estimated median time to publication was 38 months in RA field, which is faster than 47 months in oncology[ 20 ], but slower than 25 months in neurology[ 21 ]. Delays in clinical trial publication hinders an important pathway for accelerating new treatments into clinical practice, policies that enable early preprint release or public posting of completed data analysis should be pursued. Indistinct RA population, unspecified upper age limit Among all included study populations, 28% clinical trials didn’t define distinct RA patients, and the enrolled participants were not limited to or didn’t clarify specific disease activity or treatment histories. On the one hand, loose eligibility criteria may create substantial facilitators to patient access to novel therapies, promote trial recruitment and completion, and increase generalizability of trial results. However, on the other hand, synchronizing for disease stage (for example, early, established or late disease) or disease activity (for example, low-to-moderate, moderate-to-high) and taking an appropriate patient treatment history, are already helping to better tailor treatments to the right patient[ 22 ]. Moreover, Efficacy and tolerability may differ between DMARD-naïve patients and patients with an inadequate response to one or more DMARDs (biological and synthetic), as well as among patients with different levels of disease activity, the indistinct enrollment might misestimate drug efficacy and safety. Furthermore, certain trials incorporated participants with active RA without providing a clear definition of what constitutes "active RA". The utilization of diverse criteria for defining active RA across studies may introduce potential bias in the results[ 23 ]. Meanwhile, we found that 38% of the trials included population with no upper age limit, which leaves the inclusion of elderly participates over 65 years in the study ambiguous. Although there were no upper age limits specified for inclusion in the clinical trial protocol, the inclusion of elderly patients in the actual implementation process could be deemed acceptable; however, disappointingly, this was not the case in reality, elderly patients were often excluded from RA RCTs[ 9 , 24 ]. It is unfair to exclude older RA patients from clinical trials solely based on age. Firstly, the rise in the number of people aged 65 years and older living with rheumatoid arthritis, as well as the complex management of multimorbidity, polypharmacy and geriatric syndromes, have brought the elderly rheumatoid arthritis population into current research focus[ 25 ]. Secondly, older RA patients seem to have a less robust response and higher risks of adverse events to drug therapy compared with younger patients[ 26 ]. Ensuring the safety and efficacy of medication for elderly patients necessitates a high regard for the data obtained from elderly participants during the clinical trial process. Yet, the vastly underrepresented of elderly RA patients in clinical trial blocked elderly RA patients have equal access to the cutting edging treatment and receive evidence-based medication. Few head-to-head comparisons, restricted placebo use We found there were only 36% active-controlled trials and 50% placebo-controlled trials. Few head-to-head trials and most placebo-controlled trials were partly attributed to favoring superiority trials over equivalence trials by international regulatory agencies and much larger sample size for active comparator trials[ 27 , 28 ]. Therefore, the placebo-controlled clinical trial could be considered appropriate and sometimes is the gold standard for the assessment of a new therapy. However, this is not the case in RA, as numerous medications are already approved for this indication, including promising bioDMARDs and tsDMARDs. Thus, head-to-head trials are promoted in RA field to determine the difference between a new therapy and existing drugs. Among those placebo-controlled trials, almost half included placebo controls with variants designs, such as add-on or early escape placebo-control, to avoided ethical concerns; and half of trials using placebo without any design were conducted within 12 weeks. From an ethical standpoint, effective therapies for RA and treatment strategies prioritizing early disease control, support for restricting patient exposure to placebos or ineffective therapies for a prolonged period. Hence, the US Food and Drug Administration, Europe Medicines Agency, and the American College of Rheumatology have mandated the restricted use of placebos within 12 weeks or the inclusion of an active comparator in studies longer than 12 weeks as principles for control selection[ 29 – 31 ]. Our study showed that the current use of placebo in RA clinical trials was well constrained. Remarkably, large placebo responses were found in RA clinical trials, approximately 30% patients in placebo arms achieving ACR20 response and higher in RA patients with background therapy, with a rising trend, more importantly, placebo response are higher in patients continued MTX as background therapy[ 32 , 33 ]. Hence, although both were placebo controls, the placebo response of an add-on placebo control and a placebo control without any design may not be the same. The placebo response is essential to interpreting treatment efficacy, particularly for RA medications with a ceiling to their therapeutic effect, and large placebo responses may explain some failure trials leading to keep effective medication from reaching the clinic. Develop new strategies to minimize the impact of placebo response in clinical trials is an essential issue. Declining ACR20, ascending DAS28, inadequate and non-recommended treat-to-target assessment We found that ACR20 and DAS28 were the most commonly used clinical outcomes. The primary concepts for assessing RA disease activity outcomes include state measures, such as the ACR/EULAR remission criteria; response measures, exemplified by the ACR 20/50/70 response metric; or continuous measures, like the DAS28, SDAI, or CDAI[ 22 ]. The ACR20 improvement criterium was mandated by the US Food and Drug Administration as primary endpoint in many RA drug approval trials[ 30 ]. But we found that clinical trials using ACR20 manifest a declining trajectory in contrast to the ascending trend delineated by DAS28, not complied with FDA guidance recommendations. In recent years, there has been considerable debate surrounding the identification of primary endpoint for RA clinical trials, especially whether ACR20 should still be used. First, although ACR20 remains the most powerful discriminator and as primary endpoint of choice in RA drug approval trials, especially when used at early time points, given the availability of effective RA therapies, higher levels of response are considered clinically important, such as ACR50, ACR70[ 11 , 34 ]. Second, an ACR20 response is a minimal threshold in the current era, endpoints should be sensitive to change to provide better discriminatory power, such as DAS28, hybrid ACR response, and other continuous variables may be more sensitive to change and provide a more suitable alternative to ACR responder index[ 35 , 36 ]. Most important, treat-to-target strategy was a recommended strategy for managing RA, but the concept of ACR20 is not consistent with treat-to-target strategy. Treat-to-target strategy is a strict monitoring of the disease activity and adjustment of management strategies when treatment outcomes are not promising, recommended by the American College of Rheumatology (ACR), European League Against Rheumatism (EULAR), and the Asia Pacific League of Associations for Rheumatology (APLAR)[ 14 , 37 , 38 ]. In 2018, European Medicines Agency have issued guideline to recommend an endpoint reflecting a target disease state (ideally remission, or low disease activity (LDA) at the minimum) should be selected as the primary endpoints for RA clinical trials, which complied with the treat-to-target strategy[ 31 ]. The purpose of treatment is achieving either remission or low disease activity, preventing joint damage or its progression, ultimately leading to the amelioration of physical function. SDAI ≤3.3 or CDAI ≤2.8 is considered the remission standard according to ACR/EULAR criteria for remission[ 39 ]. Nonetheless, it is frustrated that few trials using treat-to-target assessment as primary clinical endpoint, and most of those assessed by DAS28 (84%). The main limitation of the DAS28 is a broader definition of remission, which is not recommended[ 40 ], and too lenient a target may precipitate suboptimal treatment strategies, consequently amplifying the disease burden. This study has some limitations. First, we only identified trials registered on ClinicalTrials.gov, which may have missed trials not subject to registration requirements and evaluations conducted in other countries. Second, we only analyzed the information obtained from the protocol, and there may be discrepancies between the clinical trial protocol and the practical clinical trial conduct, and the results may be biased. Conclusions In conclusion, we provided a comprehensive overview of the current landscape of RA interventional trials, finding the decreased RA clinical trials, excessive industry sponsorship, delayed publication, indistinct target population, unspecified upper age limit, few head-to-head comparisons, and inadequate and non-recommended treat-to-target assessment. To foster the development of effective therapies and prevent the wastage of valuable patient and financial resources, future improvement in RA trials may focus on the stratification of target population based on disease activity or treatment history to achieve precision in treatment, considerations on the adoption of more rigorous or sensitive clinical endpoints to enhance discriminatory capacity, and reconciliation of discrepancies between the endpoints selected for treat-to-target and those recommended by guidelines to optimize treatment strategy. Abbreviations RA Rheumatoid arthritis bioDMARDs Biologic disease modifying antirheumatic drugs tsDMARDs Targeted synthetic disease modifying antirheumatic drugs STROBE Strengthening the Reporting of Observational Studies in Epidemiology IQRs Interquartile ranges ACR American College of Rheumatology EULAR European League Against Rheumatism APLAR Asia Pacific League of Associations for Rheumatology LDA Low disease activity Declarations Ethics approval and consent to participate This is secondary research on existing public data that involves no human participants, individual data, or any specimens. Ethical approval and informed consent were not required according to Peking University Institutional Review Board regulations. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analyzed during the current study are available in the ClinicalTrials.gov. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Prof Nie had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Conception and design: X.Y.N., L.W.S., and Y.F. Screening database and extracting data: G.C.W. and X.W.H. Resolving disagreements and checking extracted data: X.W. Analysis and interpretation of the data: W.L.D. and X.Y.N. Drafting of the article: W.L.D. Critical revision of the article for important intellectual content: X.Y.N., L.W.S., and Y.F. Final approval of the article: W.L.D., G.C.W., X.W.H., X.W., Y.F., L.W.S., and X.Y.N. Acknowledgements Not applicable References Conrad, N., et al., Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK. 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Lau, C.S., et al., 2018 update of the APLAR recommendations for treatment of rheumatoid arthritis. International Journal of Rheumatic Diseases, 2019. 22 (3): p. 357-375. Fraenkel, L., et al., 2021 American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis. Arthritis & Rheumatology, 2021. 73 (7): p. 1108-1123. Studenic, P., et al., American College of Rheumatology/EULAR remission criteria for rheumatoid arthritis: 2022 revision. Annals of the Rheumatic Diseases, 2023. 82 (1): p. 74-80. Gaujoux-Viala, C., et al., Evaluating disease activity in rheumatoid arthritis: Which composite index is best? A systematic literature analysis of studies comparing the psychometric properties of the DAS, DAS28, SDAI and CDAI. Joint Bone Spine, 2012. 79 (2): p. 149-155. Tables Table 1. Key characteristics of RA trials registered on ClinicalTrials.gov. Characteristic N % Median time on ClinicalTrials.gov (month, interquartile range) 23 (15 - 33) Median sample size on ClinicalTrials.gov (month, interquartile range) 120 (48 - 300) Status Completed 265 62 Ongoing 160 38 Phases Early Phase I 2 0.5 Phase I 105 26 Phase I/II 21 5 Phase II 99 25 Phase II/III 13 3 Phase III 120 30 Phase IV 51 13 NA 14 4 Study Results (2 years after trial completion) Has results 132 57 No results available 99 43 Study Allocation Randomized 346 81 Non-Randomized 18 4 NA 61 14 Intervention Model Parallel Assignment 319 75 Sigle Group Assignment 67 16 Sequential Assignment 24 6 Crossover Assignment 14 3 Masking Open label 145 34 Single-blind 12 3 Double-blind or more 268 63 Funding Resource Industry * 308 73 Academia and government # 90 21 Joint sponsorship 27 6 *: Any industry sponsorship counts as industry, although it includes some academic sponsorship #: Investigator/foundation/university/hospital NA: not available. Table 2. Characteristics of target population in RA trials registered on ClinicalTrials.gov. Target population characteristics Clinical trials classified by trial phases* All (N=425) Early Phase I and Phase I (N=107) Phase I/II and Phase II (N=120) Phase II/III and Phase III (N=133) Phase IV (N=51) Healthy volunteers 63 (15%) 57 (53%) 3 (3%) 2 (2%) 0 RA 121 (28%) 34 (32%) 30 (25%) 40 (30%) 16 (31%) Active RA 68 (16%) 1 (0.9%) 33 (28%) 26 (20%) 7 (14%) Early RA 11 (3%) 1 (0.9%) 2 (2%) 3 (2%) 5 (10%) Established RA 3 (0.7%) 0 0 1 (0.8%) 1 (2%) Disease activity Low to moderate disease activity 6 (1%) 1 (0.9%) 0 2 (2%) 1 (2%) Moderate to high disease activity 112 (26%) 14 (14%) 38 (32%) 42 (32%) 15 (29%) Remission/low disease activity 3 (0.7%) 0 1 (0.8%) 0 1 (2%) Treatment history csDMARDs-IR 34 (8%) 3 (3%) 14 (12%) 16 (12%) 5 (10%) csDMARDs/bioDMARDs-IR 29 (7%) 2 (2%) 9 (8%) 12 (9%) 4 (8%) csDMARDs/bioDMARDs/tsDMARDs-IR 9 (2%) 1 (1%) 4 (3%) 3 (2%) 1 (2%) csDMARDs-naïve 2 (0.5%) 0 1 (0.8%) 0 1 (2%) bioDMARDs-naïve 2 (0.5%) 0 0 2 (2%) 0 Upper age limits < 65 years old 65 (15%) 51 (48%) 9 (8%) 3 (2%) 0 ≥ 65 years old 199 (47%) 46 (43%) 75 (63%) 51 (38%) 18 (35%) Without upper age limits 161 (38%) 10 (9%) 36 (30%) 79 (59%) 33 (65%) Gender Both male and female 396 (93%) 86 (80%) 116 (97%) 132 (99%) 50 (98%) Only male 19 (4%) 18 (17%) 0 0 0 Only female 10 (2%) 3 (3%) 4 (3%) 1 (0.8%) 1 (2%) *: not available (NA) trial was not listed in the table. RA: rheumatoid arthritis. csDMARDs: conventional synthetic disease modifying antirheumatic drugs. bioDMARDs: biologic disease modifying antirheumatic drugs. tsDMARDs: targeted synthetic disease modifying antirheumatic drugs. IR: inadequate response. Table 3. Characteristics of control groups in RA trials registered on ClinicalTrials.gov. Control group characteristics Clinical trials classified by trial phases* All (N=425) Early Phase I and Phase I (N=107) Phase I/II and Phase II (N=120) Phase II/III and Phase III (N=133) Phase IV (N=51) No treatment control 61 (14%) 21 (20%) 14 (12%) 17 (14%) 5 (10%) Active control 154 (36%) 30 (28%) 13 (11%) 70 (58%) 36 (71%) Placebo control with design Placebo control with add-on design 82 (19%) 4 (4%) 37 (31%) 32 (27%) 6 (12%) Placebo control with early-escape design 9 (2%) 0 1 (0.8%) 8 (7%) 0 Placebo control with double-dummy design 8 (2%) 0 3 (3%) 4 (3%) 1 (2%) Placebo control without any design 111 (26%) 51 (48%) 52 (43%) 2 (2%) 3 (6%) ①Subjects using placebo within 12 weeks 98 (23%) 44 (41%) 48 (40%) 2 (2%) 1 (2%) ②Subjects using placebo 12-24 weeks 7 (2%) 2 (2%) 3 (3%) 0 2 (4%) ③Subjects using placebo more than 24 weeks 6 (1%) 5 (5%) 1 (0.8%) 0 0 Table 4. Characteristics of endpoints in RA trials registered on ClinicalTrials.gov. Endpoints characteristics Clinical trials classified by trial phases* All (N=425) Early Phase I and Phase I (N=107) Phase I/II and Phase II (N=120) Phase II/III and Phase III (N=133) Phase IV (N=51) Safety 115 (27%) 59 (59%) 33 (28%) 19 (14%) 3 (6%) PK/PD 68 (16%) 55 (55%) 5 (4%) 7 (5%) 0 ACR20 104 (24%) 1 (0.9%) 41 (34%) 54 (41%) 5 (10%) ACR50 19 (4%) 0 10 (8%) 7 (5%) 2 (4%) ACR70 1 (0.2%) 0 1 (0.8%) 0 0 ACR20/50/70 14 (3%) 1 (0.9%) 10 (8%) 2 (1%) 1 (2%) DAS28 88 (21%) 2 (2%) 35 (29%) 33 (25%) 21 (41%) SDAI 7 (2%) 0 4 (3%) 3 (2%) 0 CDAI 11 (3%) 0 5 (4%) 4 (3%) 2 (4%) Remission or low disease activity 31 (7%) 0 4 (3%) 14 (11%) 10 (20%) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4674898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331636403,"identity":"9d820d37-88aa-4472-a952-76448f8d7f80","order_by":0,"name":"Wenliang Dong","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Wenliang","middleName":"","lastName":"Dong","suffix":""},{"id":331636406,"identity":"449b0b5c-33a6-4d0f-9ad0-05dd661b7794","order_by":1,"name":"Gengchen Wang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Gengchen","middleName":"","lastName":"Wang","suffix":""},{"id":331636409,"identity":"42922996-f816-4d80-b943-6496d6f751e6","order_by":2,"name":"Xiaowen Hu","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Xiaowen","middleName":"","lastName":"Hu","suffix":""},{"id":331636410,"identity":"af43af42-341e-40f1-8993-a372c558a098","order_by":3,"name":"Xue Wang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Wang","suffix":""},{"id":331636415,"identity":"10ff1648-bf52-42c4-8b26-6cbcf930cfb1","order_by":4,"name":"Yi Fang","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Fang","suffix":""},{"id":331636420,"identity":"ed42a2bc-9e38-47e7-8491-2186d33e3f94","order_by":5,"name":"Luwen Shi","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Luwen","middleName":"","lastName":"Shi","suffix":""},{"id":331636421,"identity":"249e1b71-84f7-42e6-be59-81eac2d1a1be","order_by":6,"name":"Xiaoyan Nie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACZhBRwWAAoiRI0HKGJC0gwNhGihZzduZj0rzz7hgbHGA+eJuHwS6PoBbLZrZkY95tz8wMDrAlW/MwJBcT1GJwmMfwMe+2wzYGB3jMpHkYDiQ2ENbC/+Ew7xyQFv5vxGrhYXzM23AY6DAeNmK1sBkbzjn2zFgSyLCcY5BMhJbzh59JvKm5Y9h3vPnhjTcVdoS1gAAT0D3QODUgRj0QMP4AaRkFo2AUjIJRgAsAACYTOM9FCz7MAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Nie","suffix":""}],"badges":[],"createdAt":"2024-07-02 14:17:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4674898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4674898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61357498,"identity":"af7f2d03-52b2-4907-a7b2-401894a58e40","added_by":"auto","created_at":"2024-07-29 21:11:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160527,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of trials selection.\u003c/p\u003e","description":"","filename":"Figure1.Theflowdiagramoftrialsselection.png","url":"https://assets-eu.researchsquare.com/files/rs-4674898/v1/349ab46eb3ac884261fd7f79.png"},{"id":61357497,"identity":"984952ba-ffa7-4747-bb7e-a460dec55ad9","added_by":"auto","created_at":"2024-07-29 21:11:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95874,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends of registrational RA trials with different clinical endpoints.\u003c/p\u003e","description":"","filename":"Figure2.TemporaltrendsofregistrationalRAtrialswithdifferentefficacyendpoints.png","url":"https://assets-eu.researchsquare.com/files/rs-4674898/v1/9d93530fc68e6e7bf00a510a.png"},{"id":65755512,"identity":"53d8e75d-fac2-4d1d-9253-8ba64a8c61c7","added_by":"auto","created_at":"2024-10-02 08:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1146298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4674898/v1/a730ba5e-0194-4531-82ff-7d77f99f0cd1.pdf"},{"id":61357527,"identity":"694d070c-ab82-4a3f-9bdd-ab879ef91b13","added_by":"auto","created_at":"2024-07-29 21:11:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":386934,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4674898/v1/62e012e864323899486b0b0a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics of rheumatoid arthritis clinical trials over past decade 2013-2023: current landscape and opportunities for improvement","fulltext":[{"header":"Background","content":"\u003cp\u003eRheumatoid arthritis (RA) is a common chronic inflammatory disease causing significant joint damage, substantial disability, morbidity, and premature mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over the past 25 years, a total of 15 biologic disease modifying antirheumatic drugs (bioDMARDs) and targeted synthetic disease modifying antirheumatic drugs (tsDMARDs) have been approved for RA treatment worldwide, including etanercept, infliximab, adalimumab, certolizumab pegol, golimumab, ozoralizumab, tocilizumab, sarilumab, anakinra, abatacept, rituximab, tofacitinib, baricitinib, upadacitinib, and filgotinib. As such, rheumatoid arthritis is set to one of dominant factors in the development of antibody and targeted drugs[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Since the approval of the first biologic drug, the management of RA has been revolutionized, leading to significantly decreased levels of disease activity and better long-term outcomes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite these advances, patients responding to drugs and achieving remission is still far from ideal. 40% patients could not respond to any RA drugs including bioDMARDs and tsDMARDs, and only 30% patients could achieve clinical remission[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRheumatoid arthritis seems to face a paradox: on the one hand, there are many kinds of innovative drugs available for RA treatment, on the other hand, there are many clinical needs that have still been unaddressed. The disconnect between the still urgent clinical unmet needs and crowded therapies can be attributed to various factors, some of which are related to the lack of full understanding to pathogenetic mechanisms and clinical obstacles inherent to RA[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nonetheless, the design of RA clinical trials was also an important barrier to progress. Primary issues about the RA trial design included the limitation of placebo use in RA clinical trials[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], characteristics gaps between patients enrolled in RCTs and real world[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], exclusion of older adults[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], positive study outcome associated with publication and timeliness of publication[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and appropriate endpoints selection[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Mounting evidence pressures on drug manufacturers and regulations to differentiate RA products in congested therapeutic markets can necessitate adequately designing clinical trials with sufficient power to answer these relevant scientific questions.\u003c/p\u003e \u003cp\u003eHere, we summarized the key characteristics of RA clinical trials listed on ClinicalTrials.gov over the past decade, including the studied medications, participants, controls, and endpoints, providing an up-to-date overview of the RA clinical trials landscape.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This retrospective observational study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. This is secondary research on existing public data that involves no human participants, individual data, or any specimens. Therefore, ethical approval and informed consent were not required according to Peking University Institutional Review Board regulations.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy\u003c/h2\u003e \u003cp\u003eWe performed a systematical search on ClinicalTrials.gov (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clinicaltrials.gov/\u003c/span\u003e\u003cspan address=\"https://clinicaltrials.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for RA trials registered between January 01, 2013 and December 31, 2023. Trials were searched for based on \u0026ldquo;Rheumatoid Arthritis\u0026rdquo; condition and \u0026ldquo;Interventional Studies (Clinical Trials)\u0026rdquo; study type. Any trials with a listed status terminated, withdrawn, or suspended, and trials with interventions other than drug or biological were all excluded. Two researchers (G.C.W. and X.W.H.) independently screened and reviewed the database of all potential trials to be included in this study based on the inclusion and exclusion criteria. Data was independently extracted and disagreements were resolved through consensus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Reporting\u003c/h2\u003e \u003cp\u003eFor each eligible trial, we recorded interventions, population characteristics, study characteristics, and endpoints and its evaluation time. For each medication, we identified the drug class, target, and detailed drug information, from YaoZhi database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.yaozh.com\u003c/span\u003e\u003cspan address=\"http://www.yaozh.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, one of the most authoritative platforms of drug information in China). We summarized the key characteristics of RA trials over the past decade. In addition, we classified trials into trial phases and trial characteristics based on their target population, control arms selection, and clinical endpoints. For target population, we focused on the levels of disease activity and treatment histories for RA patients, age limits and gender in the eligibility criteria. For control groups, we evaluated the active/placebo control and utilization and duration of placebo administration. For clinical endpoints, we assessed the variation of the endpoints used in RA trials supporting drug clinical efficacy evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were calculated for trial key characteristics. Categorical variables were described as frequencies and percentages, whereas continuous ones as medians and interquartile ranges (IQRs). Analyses were performed using R version 4.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFrom January 2013 to December 2023, 425 trials with 103,431 participants were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the number of RA clinical trials, particularly for bioDMARDs, has declined in the past decade, bioDMARDs continue to dominate in the RA field, accounting for 47% of all RA clinical trials (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2). Among these bioDMARDs clinical trials, the primary focus has been on the advancement of monoclonal antibodies, followed by fusion proteins (Figure S2). Biosimilars clinical trials accounted for 28% of bioDMARDs, mainly adalimumab, followed by infliximab and rituximab (Figure S3). The target molecular for the new drug trials are highly focused in the past ten years \u0026mdash;JAK, TNF, IL-6/IL-6R, BTK, CD20\u0026mdash; accounting for 48% of all RA clinical trials (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTrial characteristics\u003c/h2\u003e \u003cp\u003eKey characteristics of the clinical trials were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median (IQRs) study duration was 12 (4\u0026ndash;13) months, and the median (IQRs) sample size was 120 (48\u0026ndash;300) participants. Of the 231 completed clinical trials by 2021, 99 (43%) did not report any results (either publication results or the results demonstrated in ClinicalTrials.gov) at 2 years after completion, enrolled more than 10,000 participants. In general, the number of phase I, II, and III clinical trials is approximately equal, however, over the past decade, there has been a decline in the number of phase III clinical trials, while the number of phase I and II clinical trials has been on the rise (Figure S4). Industry was the most common sponsor (73%), followed by academia and government (21%), however, there has been a discernible decline in the quantity of industry-funded RA clinical trials over the preceding decade (Figure S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy populations\u003c/h2\u003e \u003cp\u003eCharacteristics of included population were listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. While 121 trials (28%) assessed treatment efficacy for general RA populations and 68 trials (16%) for general active RA patients, 121 trials (28%) focused on RA patients with different disease activity and 77 trials (18%) were for RA patients with distinct treatment histories. Moderate-to-high disease activity (26%) was the most common subpopulation in RA clinical trials, with an increasing trend in past decade. A total of 199 trials (47%) had an upper age limit of 65 years or more and 161 trials (38%) without an upper age limit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eControl groups\u003c/h2\u003e \u003cp\u003eControl groups in RA clinical trials over the past decade were depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Of all trials, 154 trials (36%) included active controls and 61 trials (14%) did not have control groups. In 210 trials (50%), investigating drugs were tested against placebo; 97 of these trials (46%) were placebo control with variants designs [81 trials (83%) with add-on design, 9 trials (9%) with early-escape design, 8 trials (8%) with double-dummy design] and 111 of these trials (53%) were placebo control without any design. Most of placebo-controlled trials without any design used placebo for no more than 12 weeks, however, 13 placebo-controlled trials without any design used placebo for more than 12 weeks, 5 trials of those included healthy volunteers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical endpoints\u003c/h2\u003e \u003cp\u003ePrimary endpoints to assess RA disease activity were listed at Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. ACR20 and DAS28 were the most commonly used outcomes to evaluate RA response and disease activity state and change score, and DAS28 endpoints increased from 27\u0026ndash;59% in 2013\u0026ndash;2023, while ACR20 endpoints decreased from 49\u0026ndash;12% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Only 31 trials (7%) included remission or low disease activity to assess RA treatment efficacy; 26 of these trials (84%) were assessed by DAS28 and only 4 trials (6%) assessed by CDAI or SDAI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we summarized the characteristics of 425 RA clinical trials registered on ClinicalTrials.gov during the past decade.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReduced clinical trials, RA drug development dilemma\u003c/h2\u003e \u003cp\u003eOver the past decade, there has been a notable decrease in the number of RA clinical trials, with declining from 54 trials in 2013 to 32 trials in 2023, which partly reflected the challenge of RA drug development. Drug development of RA is challenged by the limited understanding of the pathogenetic mechanisms underpinning RA clinical heterogeneity and the specific pathways driving disease in different patients, along with the lack of biomarkers to predict response to RA drugs[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It helps that certain pioneering drug products for the crowded targets have proven transformative for patients, such as TNF inhibitors, JAK inhibitors, and IL-6/IL-6R agents, however, approximately 40% of patients exhibit no response to individual DMARDs, including bioDMARDs and tsDMARDs, which called \u0026lsquo;ceiling\u0026rsquo; treatment response[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The declined number of industry-funded RA trials illustrated part of the dilemma in current RA clinical trials. The \u0026lsquo;ceiling\u0026rsquo; treatment response, together with the high cost and complexity of RCTs, pharmaceutical companies have hesitated to allocate resources to extensive trials that may only yield response rates comparable to existing drugs, and in some instances they have de-prioritized developing RA drugs[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The advancement of novel drugs with diverse targets (BTK, PD-1, GM-CSF et al) or alternative technologies (cell therapy, bispecific antibody, ADC, nano antibody) were aspired to surmount the present impasse.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExcessive industry sponsorship, delayed published results\u003c/h2\u003e \u003cp\u003eAbout three quarters of the RA trials were industry-funded. Previous studies concluded that industry support appeared to be associated with differences in trial design, results, and reporting[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, Industry sponsorship bias comparator selection, favoring products from the same company, and leads to more favorable efficacy results and conclusions than sponsorship by other sources[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Many clinical trials are often either never published or published after a significant delay. We also identified that only half of RA trials yielded results available for at least 2 years after completion during past decade. Although we did not explore the publication rate and time to publication, khan\u0026rsquo;s study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] demonstrated that the estimated median time to publication was 38 months in RA field, which is faster than 47 months in oncology[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], but slower than 25 months in neurology[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Delays in clinical trial publication hinders an important pathway for accelerating new treatments into clinical practice, policies that enable early preprint release or public posting of completed data analysis should be pursued.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIndistinct RA population, unspecified upper age limit\u003c/h2\u003e \u003cp\u003eAmong all included study populations, 28% clinical trials didn\u0026rsquo;t define distinct RA patients, and the enrolled participants were not limited to or didn\u0026rsquo;t clarify specific disease activity or treatment histories. On the one hand, loose eligibility criteria may create substantial facilitators to patient access to novel therapies, promote trial recruitment and completion, and increase generalizability of trial results. However, on the other hand, synchronizing for disease stage (for example, early, established or late disease) or disease activity (for example, low-to-moderate, moderate-to-high) and taking an appropriate patient treatment history, are already helping to better tailor treatments to the right patient[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, Efficacy and tolerability may differ between DMARD-na\u0026iuml;ve patients and patients with an inadequate response to one or more DMARDs (biological and synthetic), as well as among patients with different levels of disease activity, the indistinct enrollment might misestimate drug efficacy and safety. Furthermore, certain trials incorporated participants with active RA without providing a clear definition of what constitutes \"active RA\". The utilization of diverse criteria for defining active RA across studies may introduce potential bias in the results[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, we found that 38% of the trials included population with no upper age limit, which leaves the inclusion of elderly participates over 65 years in the study ambiguous. Although there were no upper age limits specified for inclusion in the clinical trial protocol, the inclusion of elderly patients in the actual implementation process could be deemed acceptable; however, disappointingly, this was not the case in reality, elderly patients were often excluded from RA RCTs[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It is unfair to exclude older RA patients from clinical trials solely based on age. Firstly, the rise in the number of people aged 65 years and older living with rheumatoid arthritis, as well as the complex management of multimorbidity, polypharmacy and geriatric syndromes, have brought the elderly rheumatoid arthritis population into current research focus[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Secondly, older RA patients seem to have a less robust response and higher risks of adverse events to drug therapy compared with younger patients[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Ensuring the safety and efficacy of medication for elderly patients necessitates a high regard for the data obtained from elderly participants during the clinical trial process. Yet, the vastly underrepresented of elderly RA patients in clinical trial blocked elderly RA patients have equal access to the cutting edging treatment and receive evidence-based medication.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFew head-to-head comparisons, restricted placebo use\u003c/h2\u003e \u003cp\u003eWe found there were only 36% active-controlled trials and 50% placebo-controlled trials. Few head-to-head trials and most placebo-controlled trials were partly attributed to favoring superiority trials over equivalence trials by international regulatory agencies and much larger sample size for active comparator trials[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, the placebo-controlled clinical trial could be considered appropriate and sometimes is the gold standard for the assessment of a new therapy. However, this is not the case in RA, as numerous medications are already approved for this indication, including promising bioDMARDs and tsDMARDs. Thus, head-to-head trials are promoted in RA field to determine the difference between a new therapy and existing drugs.\u003c/p\u003e \u003cp\u003eAmong those placebo-controlled trials, almost half included placebo controls with variants designs, such as add-on or early escape placebo-control, to avoided ethical concerns; and half of trials using placebo without any design were conducted within 12 weeks. From an ethical standpoint, effective therapies for RA and treatment strategies prioritizing early disease control, support for restricting patient exposure to placebos or ineffective therapies for a prolonged period. Hence, the US Food and Drug Administration, Europe Medicines Agency, and the American College of Rheumatology have mandated the restricted use of placebos within 12 weeks or the inclusion of an active comparator in studies longer than 12 weeks as principles for control selection[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our study showed that the current use of placebo in RA clinical trials was well constrained. Remarkably, large placebo responses were found in RA clinical trials, approximately 30% patients in placebo arms achieving ACR20 response and higher in RA patients with background therapy, with a rising trend, more importantly, placebo response are higher in patients continued MTX as background therapy[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Hence, although both were placebo controls, the placebo response of an add-on placebo control and a placebo control without any design may not be the same. The placebo response is essential to interpreting treatment efficacy, particularly for RA medications with a ceiling to their therapeutic effect, and large placebo responses may explain some failure trials leading to keep effective medication from reaching the clinic. Develop new strategies to minimize the impact of placebo response in clinical trials is an essential issue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDeclining ACR20, ascending DAS28, inadequate and non-recommended treat-to-target assessment\u003c/h2\u003e \u003cp\u003eWe found that ACR20 and DAS28 were the most commonly used clinical outcomes. The primary concepts for assessing RA disease activity outcomes include state measures, such as the ACR/EULAR remission criteria; response measures, exemplified by the ACR 20/50/70 response metric; or continuous measures, like the DAS28, SDAI, or CDAI[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The ACR20 improvement criterium was mandated by the US Food and Drug Administration as primary endpoint in many RA drug approval trials[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. But we found that clinical trials using ACR20 manifest a declining trajectory in contrast to the ascending trend delineated by DAS28, not complied with FDA guidance recommendations. In recent years, there has been considerable debate surrounding the identification of primary endpoint for RA clinical trials, especially whether ACR20 should still be used. First, although ACR20 remains the most powerful discriminator and as primary endpoint of choice in RA drug approval trials, especially when used at early time points, given the availability of effective RA therapies, higher levels of response are considered clinically important, such as ACR50, ACR70[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Second, an ACR20 response is a minimal threshold in the current era, endpoints should be sensitive to change to provide better discriminatory power, such as DAS28, hybrid ACR response, and other continuous variables may be more sensitive to change and provide a more suitable alternative to ACR responder index[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Most important, treat-to-target strategy was a recommended strategy for managing RA, but the concept of ACR20 is not consistent with treat-to-target strategy.\u003c/p\u003e \u003cp\u003eTreat-to-target strategy is a strict monitoring of the disease activity and adjustment of management strategies when treatment outcomes are not promising, recommended by the American College of Rheumatology (ACR), European League Against Rheumatism (EULAR), and the Asia Pacific League of Associations for Rheumatology (APLAR)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In 2018, European Medicines Agency have issued guideline to recommend an endpoint reflecting a target disease state (ideally remission, or low disease activity (LDA) at the minimum) should be selected as the primary endpoints for RA clinical trials, which complied with the treat-to-target strategy[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The purpose of treatment is achieving either remission or low disease activity, preventing joint damage or its progression, ultimately leading to the amelioration of physical function. SDAI \u0026le;3.3 or CDAI \u0026le;2.8 is considered the remission standard according to ACR/EULAR criteria for remission[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Nonetheless, it is frustrated that few trials using treat-to-target assessment as primary clinical endpoint, and most of those assessed by DAS28 (84%). The main limitation of the DAS28 is a broader definition of remission, which is not recommended[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and too lenient a target may precipitate suboptimal treatment strategies, consequently amplifying the disease burden.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, we only identified trials registered on ClinicalTrials.gov, which may have missed trials not subject to registration requirements and evaluations conducted in other countries. Second, we only analyzed the information obtained from the protocol, and there may be discrepancies between the clinical trial protocol and the practical clinical trial conduct, and the results may be biased.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e In conclusion, we provided a comprehensive overview of the current landscape of RA interventional trials, finding the decreased RA clinical trials, excessive industry sponsorship, delayed publication, indistinct target population, unspecified upper age limit, few head-to-head comparisons, and inadequate and non-recommended treat-to-target assessment. To foster the development of effective therapies and prevent the wastage of valuable patient and financial resources, future improvement in RA trials may focus on the stratification of target population based on disease activity or treatment history to achieve precision in treatment, considerations on the adoption of more rigorous or sensitive clinical endpoints to enhance discriminatory capacity, and reconciliation of discrepancies between the endpoints selected for treat-to-target and those recommended by guidelines to optimize treatment strategy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003ebioDMARDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eBiologic disease modifying antirheumatic drugs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003etsDMARDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eTargeted synthetic disease modifying antirheumatic drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003eSTROBE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003eIQRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eInterquartile ranges\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003eACR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eAmerican College of Rheumatology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003eEULAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean League Against Rheumatism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003eAPLAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eAsia Pacific League of Associations for Rheumatology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.518672199170126%\" valign=\"top\"\u003e\n \u003cp\u003eLDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.48132780082987%\" valign=\"top\"\u003e\n \u003cp\u003eLow disease activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is secondary research on existing public data that involves no human participants, individual data, or any specimens. Ethical approval and informed consent were not required according to Peking University Institutional Review Board regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the ClinicalTrials.gov.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProf Nie had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003eConception and design: X.Y.N., L.W.S., and Y.F.\u003c/p\u003e\n\u003cp\u003eScreening database and extracting data: G.C.W. and X.W.H.\u003c/p\u003e\n\u003cp\u003eResolving disagreements and checking extracted data: X.W.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of the data: W.L.D. and X.Y.N.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDrafting of the article: W.L.D.\u003c/p\u003e\n\u003cp\u003eCritical revision of the article for important intellectual content:\u0026nbsp;X.Y.N., L.W.S., and Y.F.\u003c/p\u003e\n\u003cp\u003eFinal approval of the article: W.L.D., G.C.W., X.W.H., X.W.,\u0026nbsp;Y.F., L.W.S., and X.Y.N.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eConrad, N., et al., \u003cem\u003eIncidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK.\u003c/em\u003e The Lancet, 2023. \u003cstrong\u003e401\u003c/strong\u003e(10391): p. 1878-1890.\u003c/li\u003e\n\u003cli\u003eMullard, A., \u003cem\u003eFDA approves 100th monoclonal antibody product.\u003c/em\u003e Nature Reviews Drug Discovery, 2021. \u003cstrong\u003e20\u003c/strong\u003e(7): p. 491-495.\u003c/li\u003e\n\u003cli\u003ePhilip, B., G.P. 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Buch, \u003cem\u003eTransforming clinical trials in rheumatology: towards patient-centric precision medicine.\u003c/em\u003e Nature Reviews Rheumatology, 2020. \u003cstrong\u003e16\u003c/strong\u003e(10): p. 590-599.\u003c/li\u003e\n\u003cli\u003eGaudino, M., et al., \u003cem\u003eCharacteristics of Contemporary Randomized Clinical Trials and Their Association With the Trial Funding Source in Invasive Cardiovascular Interventions.\u003c/em\u003e Jama Internal Medicine, 2020. \u003cstrong\u003e180\u003c/strong\u003e(7): p. 993-1001.\u003c/li\u003e\n\u003cli\u003eLathyris, D.N., et al., \u003cem\u003eIndustry sponsorship and selection of comparators in randomized clinical trials.\u003c/em\u003e European Journal of Clinical Investigation, 2010. \u003cstrong\u003e40\u003c/strong\u003e(2): p. 172-182.\u003c/li\u003e\n\u003cli\u003eLundh, A., et al., \u003cem\u003eIndustry sponsorship and research outcome.\u003c/em\u003e Cochrane Database of Systematic Reviews, 2017(2).\u003c/li\u003e\n\u003cli\u003eChapman, P.B., et al., \u003cem\u003eTime to publication of oncology trials and why some trials are never published.\u003c/em\u003e Plos One, 2017. \u003cstrong\u003e12\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003eBrown, J., et al., \u003cem\u003eTime to Publication of Clinical Trials Funded by the National Institute of Neurological Disorders and Stroke.\u003c/em\u003e Ann Neurol, 2021. \u003cstrong\u003e90\u003c/strong\u003e(6): p. 861-864.\u003c/li\u003e\n\u003cli\u003eAletaha, D., \u003cem\u003ePrecision medicine and management of rheumatoid arthritis.\u003c/em\u003e Journal of Autoimmunity, 2020. \u003cstrong\u003e110\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eEzard, C., et al., \u003cem\u003eWhat is meant by active disease in the NICE recommendation on use of combination therapy in early RA?\u003c/em\u003e Rheumatology, 2012. \u003cstrong\u003e51\u003c/strong\u003e(5): p. 947-948.\u003c/li\u003e\n\u003cli\u003eStrait, A., et al., \u003cem\u003eDemographic Characteristics of Participants in Rheumatoid Arthritis Randomized Clinical Trials A Systematic Review.\u003c/em\u003e Jama Network Open, 2019. \u003cstrong\u003e2\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003evan Onna, M. and A. Boonen, \u003cem\u003eChallenges in the management of older patients with inflammatory rheumatic diseases.\u003c/em\u003e Nature Reviews Rheumatology, 2022. \u003cstrong\u003e18\u003c/strong\u003e(6): p. 326-334.\u003c/li\u003e\n\u003cli\u003eIshchenko, A. and R.J. Lories, \u003cem\u003eSafety and Efficacy of Biological Disease-Modifying Antirheumatic Drugs in Older Rheumatoid Arthritis Patients: Staying the Distance.\u003c/em\u003e Drugs Aging, 2016. \u003cstrong\u003e33\u003c/strong\u003e(6): p. 387-98.\u003c/li\u003e\n\u003cli\u003eFood and H.H.S. Drug Administration, \u003cem\u003eInternational Conference on Harmonisation; choice of control group and related issues in clinical trials; availability. Notice.\u003c/em\u003e Fed Regist, 2001. \u003cstrong\u003e66\u003c/strong\u003e(93): p. 24390-1.\u003c/li\u003e\n\u003cli\u003ePearson, S.D., \u003cem\u003ePlacebo-controlled trials, ethics, and the goals of comparative effectiveness research: comment on \u0026quot;lack of head-to-head trials and fair control arms\u0026quot;.\u003c/em\u003e Arch Intern Med, 2012. \u003cstrong\u003e172\u003c/strong\u003e(3): p. 244-5.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Dell, J.R., et al., \u003cem\u003eAmerican College of Rheumatology Clinical Trial Priorities and Design Conference, July 22-23, 2010.\u003c/em\u003e Arthritis and Rheumatism, 2011. \u003cstrong\u003e63\u003c/strong\u003e(8): p. 2151-2156.\u003c/li\u003e\n\u003cli\u003eUS Food and Drug Administration.\u003cem\u003e Guidance for industry; rheumatoid arthritis: developing drug products for industry.\u003c/em\u003e 2013. Available at https://www.fda.gov/media/86066/download. Accessed April 18,2024.\u003c/li\u003e\n\u003cli\u003eEuropean Medicines Agency. \u003cem\u003eGuideline on clinical investigation of medical products for the treatment of rheumatoid arthritis.\u003c/em\u003e 2018. Available at https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-clinical-investigation-medicinal-products-treatment-rheumatoid-arthritis_en.pdf. Accessed April 18,2024.\u003c/li\u003e\n\u003cli\u003eBechman, K., et al., \u003cem\u003ePlacebo Response in Rheumatoid Arthritis Clinical Trials.\u003c/em\u003e Journal of Rheumatology, 2020. \u003cstrong\u003e47\u003c/strong\u003e(1): p. 28-34.\u003c/li\u003e\n\u003cli\u003eKerschbaumer, A., et al., \u003cem\u003eImpact of pre-existing background therapy on placebo responses in randomised controlled clinical trials of rheumatoid arthritis.\u003c/em\u003e Annals of the Rheumatic Diseases, 2022. \u003cstrong\u003e81\u003c/strong\u003e(10): p. 1374-1378.\u003c/li\u003e\n\u003cli\u003eChung, C.P., et al., \u003cem\u003eAre American College of Rheumatology 50% response criteria superior to 20% criteria in distinguishing active aggressive treatment in rheumatoid arthritis clinical trials reported since 1997? A meta-analysis of discriminant capacities.\u003c/em\u003e Annals of the Rheumatic Diseases, 2006. \u003cstrong\u003e65\u003c/strong\u003e(12): p. 1602-1607.\u003c/li\u003e\n\u003cli\u003eFelson, D.T. and M.P. LaValley, \u003cem\u003eThe ACR20 and defining a threshold for response in rheumatic diseases: too much of a good thing.\u003c/em\u003e Arthritis Research \u0026amp; Therapy, 2014. \u003cstrong\u003e16\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eWard, M.M., et al., \u003cem\u003eOrigins of Discordant Responses among 3 Rheumatoid Arthritis Improvement Criteria.\u003c/em\u003e Journal of Rheumatology, 2018. \u003cstrong\u003e45\u003c/strong\u003e(6): p. 745-752.\u003c/li\u003e\n\u003cli\u003eLau, C.S., et al., \u003cem\u003e2018 update of the APLAR recommendations for treatment of rheumatoid arthritis.\u003c/em\u003e International Journal of Rheumatic Diseases, 2019. \u003cstrong\u003e22\u003c/strong\u003e(3): p. 357-375.\u003c/li\u003e\n\u003cli\u003eFraenkel, L., et al., \u003cem\u003e2021 American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis.\u003c/em\u003e Arthritis \u0026amp; Rheumatology, 2021. \u003cstrong\u003e73\u003c/strong\u003e(7): p. 1108-1123.\u003c/li\u003e\n\u003cli\u003eStudenic, P., et al., \u003cem\u003eAmerican College of Rheumatology/EULAR remission criteria for rheumatoid arthritis: 2022 revision.\u003c/em\u003e Annals of the Rheumatic Diseases, 2023. \u003cstrong\u003e82\u003c/strong\u003e(1): p. 74-80.\u003c/li\u003e\n\u003cli\u003eGaujoux-Viala, C., et al., \u003cem\u003eEvaluating disease activity in rheumatoid arthritis: Which composite index is best? A systematic literature analysis of studies comparing the psychometric properties of the DAS, DAS28, SDAI and CDAI.\u003c/em\u003e Joint Bone Spine, 2012. \u003cstrong\u003e79\u003c/strong\u003e(2): p. 149-155.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Key characteristics of RA trials registered on ClinicalTrials.gov.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eMedian time on ClinicalTrials.gov (month, interquartile range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e(15 - 33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eMedian sample size on ClinicalTrials.gov (month, interquartile range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e(48 - 300)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eOngoing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eEarly Phase I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003ePhase I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003ePhase I/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003ePhase II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003ePhase II/III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003ePhase III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003ePhase IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Results (2 years after trial completion)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eHas results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eNo results available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Allocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eRandomized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eNon-Randomized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eParallel Assignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eSigle Group Assignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eSequential Assignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eCrossover Assignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMasking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eOpen label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eSingle-blind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eDouble-blind or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunding Resource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eIndustry\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eAcademia and government\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\"\u003e\n \u003cp\u003eJoint sponsorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*: Any industry sponsorship counts as industry, although it includes some academic sponsorship\u003c/p\u003e\n\u003cp\u003e#: Investigator/foundation/university/hospital\u003c/p\u003e\n\u003cp\u003eNA: not available.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Characteristics of target population in RA trials registered on ClinicalTrials.gov.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.838709677419356%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget population characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.16129032258064%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trials classified by trial phases*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=425)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly Phase I and Phase I (N=107)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase I/II and Phase II (N=120)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.285714285714285%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase II/III and Phase III (N=133)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase IV (N=51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eHealthy volunteers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e63 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e57 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e3 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e121 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e34 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e30 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e40 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e16 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eActive RA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e68 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e33 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e26 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e7 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eEarly RA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e11 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e5 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eEstablished RA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e3 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eLow to moderate disease activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e6 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eModerate to high disease activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e112 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e14 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e38 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e42 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e15 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eRemission/low disease activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e3 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003ecsDMARDs-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e34 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e3 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e14 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e16 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e5 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003ecsDMARDs/bioDMARDs-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e29 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e9 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e12 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e4 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003ecsDMARDs/bioDMARDs/tsDMARDs-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e9 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e1 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003ecsDMARDs-na\u0026iuml;ve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e2 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003ebioDMARDs-na\u0026iuml;ve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e2 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper age limits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003e\u0026lt; 65 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e65 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e51 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e9 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003e\u0026ge; 65 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e199 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e46 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e75 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e51 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e18 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eWithout upper age limits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e161 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e10 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e36 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e79 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e33 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eBoth male and female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e396 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e86 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e116 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e132 (99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e50 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eOnly male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e19 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e18 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003eOnly female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66702470461869%\"\u003e\n \u003cp\u003e10 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.33404940923738%\"\u003e\n \u003cp\u003e3 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.508055853920517%\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.385606874328678%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*: not available (NA) trial was not listed in the table.\u003c/p\u003e\n\u003cp\u003eRA: rheumatoid arthritis. csDMARDs: conventional synthetic disease modifying antirheumatic drugs. bioDMARDs: biologic disease modifying antirheumatic drugs. tsDMARDs: targeted synthetic disease modifying antirheumatic drugs. IR: inadequate response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Characteristics of control groups in RA trials registered on ClinicalTrials.gov.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"69.13978494623656%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trials classified by trial phases*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.597200622083982%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=425)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.038880248833593%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly Phase I and Phase I (N=107)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.48367029548989%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase I/II and Phase II (N=120)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.038880248833593%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase II/III and Phase III (N=133)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase IV (N=51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003eNo treatment control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e61 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e21 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e14 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e17 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e5 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003eActive control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e154 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e30 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e13 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e70 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e36 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlacebo control with design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003ePlacebo control with add-on design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e82 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e4 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e37 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e32 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e6 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003ePlacebo control with early-escape design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e9 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e8 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003ePlacebo control with double-dummy design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e8 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e3 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlacebo control without any design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e111 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e51 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e52 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e3 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003e①Subjects using placebo within 12 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e98 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e44 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e48 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003e②Subjects using placebo 12-24 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e7 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e3 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e2 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.86021505376344%\"\u003e\n \u003cp\u003e③Subjects using placebo more than 24 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709677419354838%\"\u003e\n \u003cp\u003e6 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e5 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.236559139784948%\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.311827956989248%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Characteristics of endpoints in RA trials registered on ClinicalTrials.gov.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.376344086021504%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndpoints characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.6236559139785%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trials classified by trial phases*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.697841726618705%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=425)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.165467625899282%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly Phase I and Phase I (N=107)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase I/II and Phase II (N=120)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.165467625899282%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase II/III and Phase III (N=133)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.244604316546763%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase IV (N=51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eSafety\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e115 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e59 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e33 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e19 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e3 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003ePK/PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e68 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e55 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e5 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e7 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eACR20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e104 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e41 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e54 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e5 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eACR50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e19 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e10 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e7 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e2 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eACR70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e1 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eACR20/50/70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e14 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e10 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e2 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eDAS28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e88 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e35 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e33 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e21 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eSDAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e7 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eCDAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e11 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e5 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e2 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\"\u003e\n \u003cp\u003eRemission or low disease activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.211600429645543%\"\u003e\n \u003cp\u003e31 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.219119226638025%\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.293233082706767%\"\u003e\n \u003cp\u003e14 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.633727175080558%\"\u003e\n \u003cp\u003e10 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, Clinical trial, Characteristics","lastPublishedDoi":"10.21203/rs.3.rs-4674898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4674898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is a disconnection between the continued pressing clinical demand for rheumatoid arthritis (RA) treatments and the saturation of the current therapeutic markets. The design of rheumatoid arthritis trials might represent one of significant barrier to advancing therapeutic progress. A comprehensive review was performed to evaluate the characteristics of RA trials registered in ClinicalTrials.gov from 2013 to 2023.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe ClinicalTrials.gov database was searched for trials focused on the RA interventional trials from 2013 to 2023. Interventional drug or biological trials were included. Key characteristics of RA trials were summarized and target population, control groups selection, and clinical endpoints were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBetween January 2013 and December 2023, 425 RA trials were included. Decreased trial numbers, excessive industry sponsorship, and delayed published results were found. For target population, 28% clinical trials didn\u0026rsquo;t define distinct RA patients, and 38% of the trials included population with no upper age limit. For control groups, only 36% trials had head-to-head comparisons, 50% were placebo-controlled, where half of placebo-controlled trials were with special design (add-on, early escape, double dummy), and half without any design. For clinical endpoints, ACR20 (24%) and DAS28 (21%) were the most commonly used outcomes, with declining ACR20 and ascending DAS28. Only 7% trials adherence to \u0026ldquo;treat-to-target\u0026rdquo; strategy, but the most commonly used outcome measures not aligned with guideline-recommended.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study contributes to a nuanced comprehension of the current landscape of RA trials and offers valuable insights for future improvement. This included the necessity of stratifying the target population based on disease activity or treatment history to achieve precision in treatment; considerations of more stringent or sensitive clinical endpoints to provide better discriminatory power; addressing discrepancies between the endpoints selected for treat-to-target and those recommended by guidelines to choose optimal treatment strategy.\u003c/p\u003e","manuscriptTitle":"Characteristics of rheumatoid arthritis clinical trials over past decade 2013-2023: current landscape and opportunities for improvement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 21:11:42","doi":"10.21203/rs.3.rs-4674898/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17cae7c7-ce3e-4613-97ef-7fc44ddf0718","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-19T06:08:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 21:11:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4674898","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4674898","identity":"rs-4674898","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}