Poor Reporting Quality and High Proportion of Missing Data in Economic Evaluations Alongside Pragmatic Trials: A Cross-sectional Survey

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Design Cross-sectional survey. Setting Data were extracted from PubMed and OVID (Embase, CENTRAL, HTA database, and NIH EED) from January 1, 2010, to April 24, 2022. Economic evaluations conducted with pRCTs were included and secondary analyses, abstracts, comments, letters, notes, editorials, protocols, subgroup analyses, pilot and feasibility trials, post-hoc analyses, and reviews were excluded. Two groups of two independent reviewers identified the relevant articles, and data were extracted from three groups of two reviewers. Main outcome measures Descriptive analyses were performed to assess characteristics of the included studies, missingness in the included studies, and handling of missing data. Results A total of 715 studies were identified, of which 152 met the inclusion criteria. Overall, 113 articles reported missing data, 119 reported missing costs, and 132 reported missing effects. More than 50% (58/113) of the articles reported the proportion or quantity of overall missingness, and 64.71% and 54.55% reported missing costs and effects, respectively. The proportion of missingness of < 5% in the overall group was 3.45%, whereas the proportions of missing costs and effects were both lower than 10% (5.26% vs. 8.45%). In terms of the proportion of missing data, the overall missingness rate was 30.22% in 58 studies, whereas the median proportion of missing data was slightly higher than that of the missing effects (30.92% vs. 27.78%). For details on dealing with missing data, 56 (36.84%) studies conducted a sensitivity analysis on handling missing data. Of these studies, 12.50% reported missing mechanisms, and 83.93% examined handling methods. Conclusions Insufficient description and reporting of missing data, along with a high proportion of missing data in pRCT-based economic evaluations, could decrease the reliability and extrapolation of conclusions, leading to misleading decision-making. Future research should include an increased sample size by fully considering the potential proportion of missing data and enhance the transparency and evidence quality of economic evaluation alongside pragmatic trials. Economic evaluations Pragmatic trials Missing data Cross-sectional survey Figures Figure 1 Introduction Missing data, which may lead to selection and information biases and jeopardize the validity, generalizability, and precision of study results, is one of the most frequent problems in clinical trials 1–3 . In trial-based economic evaluations, the rate of data integrity in economic outcomes may be much lower than that in clinical outcomes. As previous trial-based economic evaluations have shown, randomized clinical trials may have data on 80–95% complete cases of clinical outcomes 4–7 , whereas only 50–80% of randomized participants have complete economic data 8–11 . The following reasons could explain why a high proportion of missing values occurred in trial-based economic evaluations. First, economic evaluations tend to collect rich, longitudinal information from individuals, such as their use of healthcare services and health-related quality of life, which usually consists of multiple questions to calculate total cost and health utility 12 13 . If one component question is missing, the total cost and health utility are also absent 14 . Therefore, missingness is more likely to occur in economic data. Second, economic variables may be considered less important by the researchers responsible for data collection, which could trigger higher rates of missingness 15 . Pragmatic randomized controlled trials (pRCTs) measure the treatment effects in routine clinical practice, closely approximating the reality of the medical environment 16 17 . The characteristic features of pRCTs include ( 1 ) selecting clinically relevant alternative interventions for comparison, ( 2 ) recruiting participants from heterogeneous practice settings, and ( 3 ) collecting data on a broad range of health outcomes, including patient-reported, global, and subjective outcomes 18 19 . Thus, well-conducted pRCT-based economic evaluations are more likely to generalize the results to an extended clinical setting and inform health-related decision-making. As the International Society for Pharmacoeconomics and Outcomes Research has proposed, pragmatic effectiveness trials are the best vehicles for economic studies 20 . However, one of the inevitable limitations of pragmatic design is that increasing non-adherence to the trial protocol, and even the use of electronic health records, results in inconsistent data collection and missing data 19–21 . Missing data may seriously compromise the credibility of causal inferences from pRCTs 22 . Although several previous studies have revealed that missing data are very common in traditional trial-based economic evaluations, and the reporting quality is poor 12 23 24 , information on the proportion, handling approaches, and reporting quality of missing data in pRCT-based economic evaluations remains unknown. Therefore, the aim of this cross-sectional analysis was two-fold: First, to provide a comprehensive qualitative description of the extent of and handling approaches to missing economic outcomes in the included pRCT-based economic evaluations published over the last 10 years. Second, to provide a critical review of reporting and potential methodological issues in handling missing data to provide suggestions on how missing data should be reported for future research and update the methodological guidelines on handling missing data in pRCT-based economic evaluations, in accordance with the CONSORT-Outcome 2022 statement, the framework, and guidelines for the treatment of missing data in economic evaluations 25–27 . Materials and methods Eligibility and exclusion criteria This study included economic evaluations of pRCTs published in peer-reviewed journals. Abstracts, comments, letters, protocols, reviews, subgroup analyses, post-hoc analyses, pilot trials, and non-English language articles were excluded. Data source and search strategy A systematic literature search was performed using PubMed, Embase, the Cochrane Central Register of Controlled Trials, the Health Technology Assessment Database, and the National Health Service Economic Evaluation Database (NHS EED), covering the period from January 1, 2010, to December 31, 2021. On April 24, 2022, a complementary search was performed based on the inclusion and exclusion criteria. Table S1 provides the complete search strategy used to find articles of original research for inclusion in our systematic review. Study selection and data extraction Titles and abstracts were screened by two sets of two independent reviewers (CW, WL, XZ, and WH). The same reviewers assessed the full texts of the identified studies for eligibility, and the reasons for exclusion were recorded. Discrepancies were resolved by consensus or by WH. A structured questionnaire was developed based on WeChat and Wenjuanxing platforms to collect data from all eligible studies. Two reviewers (YX and RS) pilot-tested the predefined form to confirm its completeness, appropriateness, and applicability. Three teams of reviewers (YX, RS, JH, WL, CW, and LZ) independently collected the data from all eligible studies. Discrepancies were resolved by consensus or by WH. The following information was collected from each eligible study. The following basic information were extracted: sample size, follow-up time, country, Journal Impact Factor Quartile, and whether economy-related outcomes were the primary endpoints of each eligible study. Information on the occurrence of missing data, proportion of missing data, missing patterns, missing mechanisms, methods of handling missingness, imputation level, and relative information of multiple imputation (MI) were also extracted. Additionally, we obtained information on whether the results of the sensitivity analysis were consistent with the primary results. Statistical analysis Descriptive analysis was conducted of all the variables. For continuous variables, data are presented as mean (standard deviation, SD) if normally distributed and otherwise as median [interquartile range, IQR] or median (range). For categorical variables, data are presented as frequencies divided by the total number and proportion. Patient and public involvement No patients or members of the public were involved in setting the research question or the outcome measures, nor were they involved in developing plans for the design or implementation of the study or asked to advise on interpretation or writing up of results. Result Selected articles Using this search strategy, a total of 715 articles were retrieved from the databases. After removing 234 duplicates, 481 studies were left for the first-round screening and 243 studies for full-text screening. Finally, 152 studies met the eligibility and exclusion criteria (Fig. 1). Study characteristics of the included studies Among the 152 included studies, the median sample size for economic evaluation was 343 (IQR, 202.5–696), which was slightly smaller than the sample size in the protocol, which was 360 (IQR, 230–750). The median follow-up period was 12 months (IQR, 6–12). The UK (n = 90, 59.21%) conducted the majority of the economic evaluations, followed by the Netherlands (n = 24, 15.79%). More than 60% of the studies (n = 98, 64.47%) were published in the top quartile of the Journal Impact Factor. About half of the studies (n = 75, 49.34%) used economy-related outcomes as primary endpoints (Table 1 ). Table 1 Study characteristics of 152 included studies Characteristics Included studies (N = 152) Sample size for the protocol (median [IQR]) 360[230–750] Sample size for economic evaluation (median [IQR]) 343[202.5–696] Follow-up (month) (median [IQR]) 12 ( 6 – 12 ) Country (Top 5), n (%) UK 90 (59.21) Netherlands 24 (15.79) USA 7 (4.61) Australia 7 (4.61) Sweden 4 (2.63) South Africa 4 (2.63) France 3 (1.97) Canada 3 (1.97) JIFQ, n (%) * Q1 98 (64.47) Q2 37 (24.34) Q3 13 (8.55) Q4 2 (1.32) Unknown 2 (1.32) Whether the economic-related outcomes were primary endpoints, n (%) Yes 75 (49.34%) No 73 (48.03%) Not reported 4 (2.63%) * JIFQ: Journal Impact Factor Quartile. Reuters divides all journals into four equal categories based on their impact factors. The journals in Q1 were the highest ranked (top 25%) in a category, and the journals in Q4 were the lowest. Basic information on missing data Of the 152 studies, approximately 75% of them included studies that missed cost and effect data simultaneously, whereas 25.66% did not report overall missingness. More specifically, missing effects were more common than missing costs (86.84% vs. 78.29%); however, the number of studies that did not mention missing costs was almost twice that of missing effects (21.05% vs. 12.50%). More than 50% (58/113) reported the proportion or quantity of overall missingness, and 64.71% and 54.55% of the studies reported missing costs and effects, respectively. The proportion of missingness of < 5% in the overall group was 3.45%, whereas the proportions of missing costs and effects were both lower than 10% (5.26% vs. 8.45%). In terms of the proportion of missing data, the overall missingness rate was 30.22% in 58 studies, whereas the median proportion of missing data was slightly higher than that of the missing effects (30.92% vs. 27.78%). According to the different lengths of the follow-up period, the median proportion of missing data in the>1-year group was higher than that of the ≤ 1-year group, particularly in the overall group (42.20 vs. 29.50%) (Table 2 ). Table 2 Basic information on missing data Basic information Number of studies, n/N (%) Overall * Cost Effects 1. The occurrence of missing data Missingness 113/152 (74.34%) 119/152 (78.29) 132/152 (86.84) None missingness 0/152 (0%) 1/152 (0.66) 1/152 (0.66) Not reported 39/152 (25.66%) 32/152 (21.05) 19/152 (12.50) 2. Reporting the quantity of missing data 58/113 (51.33) 77/119 (64.71) 72/132 (54.55) 2.1 The proportion of missingness < 5% 2/58 (3.45%) 4/76 (5.26%) 6/71 (8.45%) 2.2 The proportion of missing data in the follow-up period (median [IQR], %) & 30.22[20.59–4.67] 30.92[15.68–46.43] 27.78[14.49–39.80] Follow-up ≤ 1 year 29.50[19.95–2.08] 30.93[14.58–46.51] 25.12[14.28–9.96] Follow-up>1 year 42.20[31.53–1.96] 31.72[19.71–45.76] 34.48[15.37–67.43] * Overall, both cost and effect data are missing. & The follow-up periods were the time points for calculating the incremental cost-effectiveness ratios. Details on dealing with missing data in the primary analyses Among the studies with missing data, approximately one-fourth reported missing mechanisms, and only a few studies further reported the reasons for the missing mechanisms. Missing at random (MAR) was the most popular assumption of missingness, with a proportion of approximately 90%. Regarding specific methods for handling missing data, 27.43% of the studies simultaneously reported methods for handling missing costs and effects. However, the percentage of studies that separately reported the methods of dealing with costs or effects was approximately 80%. More specifically, MI was the most frequently used method (66.32% vs. 70.30%), followed by single imputation (20.62% vs. 16.83%) and deletion (19.59% vs. 12.87%) (Table 3 ). Table 3 Detailed information on handling missing data in primary analyses Characteristics Number of studies, n/N (%) Overall Cost Effects 1. Reporting missing mechanism * 31/113 (27.43) 25/119 (21.01) 31/132 (23.48) MAR 27/31 (87.10) 23/25 (92.00) 28/31 (90.32) MCAR 3/31 (9.68) 1/25 (4.00) 2/31 (6.45) MNAR 1/31 (3.23) 1/25 (4.00) 1/31 (3.23) 2. Reporting reasons for missing mechanism selection 5/31 (16.13) 5/25 (20.00) 5/31 (16.13) 3. Reporting methods of handling missingness 31/113 (27.43) 97/119 (81.51) 101/132 (76.52) MI 63/97 (66.32) 71/101 (70.30) Single imputation 20/97 (20.62) 17/101 (16.83) Mean imputation 14/20 (70.00) 10/17 (58.82) LOCF 2/20 (10.00) 4/17 (23.53) Regression imputation 2/20 (10.00) 2/17 (11.76) Deletion 19/97 (19.59) 13/101 (12.87) CCA 18/19 (94.74) 12/13 (92.31) ACA 1/19 (5.26) 1/13 (7.69) MAR, missing at random; MCAR, missing completely at random; MNAR, missing not at random; MI, multiple imputation; LOCF, last observation carried forward; CCA, complete case analysis; ACA, available case analysis. * One study assumed different mechanisms for the missing costs and effects. Details on dealing with missing data in the sensitivity analyses Fifty-six (36.84%) studies conducted sensitivity analyses to address missing data. Of these studies, 12.50% reported missing mechanisms, and 83.93% reported handling methods. In contrast to the primary analyses, complete care analysis was the most popular method for dealing with missing data in the sensitivity analyses. The results of the sensitivity analysis were consistent with those of the primary analyses in 94.64% of the studies (Table 4 ). Table 4 Detailed information on sensitivity analysis for missing data Characteristics Number of studies, n/N (%) 1. Conducting sensitivity analysis 56/152 (36.84) 2. Reporting missing mechanism 7/56 (12.50) MNAR 5/7 (71.43) MCAR 0 (0) MAR 2/7 (28.57) 3. Handling methods of missing data 47/56 (83.93) CCA 28/47 MI 16/47 Single imputation 7/47 4. Whether the results of the sensitivity analysis were consistent with the primary results Yes 53/56 (94.64) No 1/56 (1.79) Not reported 2/56 (3.57) MAR, missing at random; MCAR, missing completely at random; MNAR, missing not at random; CCA, complete case analysis; MI, multiple imputations. Discussion Statement of principal findings This study included a cross-sectional analysis of the reporting quality and quantity of missing data in pRCT-based economic evaluations published from 2010 to 2022 and found that the reporting quality of missing data in pRCT-based economic evaluations was insufficient. Although most studies claimed missing values, studies reporting a specific quantity of missing data accounted for approximately 60%. Additionally, the percentage was lower in studies with missing costs and effects. In terms of methods for handling missing data, the reporting quality in the primary analyses was higher than that in the sensitivity analyses. Furthermore, missing data were obviously serious in pragmatic trial-based economic evaluations; nearly 90% of studies acknowledged missing data regardless of missing costs or effects, and less than 4% of studies had a missingness proportion of < 5%. The median percentage of missing data was approximately 30%, and the third quartile was 67.43%. Moreover, the longer the follow-up period, the higher the proportion of missing data, particularly in studies with missing costs and effects. Comparison to similar studies Over the past decade, missing data in piggyback economic evaluations have raised widespread concerns. Previous studies have found poorly reported and unclear methodologies for missing data in cost-effectiveness analyses alongside RCTs 14 27 . Moreover, recent analyses on the statistical quality of handling missing data in trial-based economic evaluations found that MI is the most common method for handling missing data under MAR mechanisms 28 , and some used only a single method without a sensitivity analysis and did not offer a justification of their approach of handling missing data 14 . These results are consistent with those of the present study. Our analysis extends previous studies by assessing reporting quality, the extent of missing data, and handling methods in pRCT-based economic evaluations. Implications for future research The findings of this study have important implications for the future economic evaluation of piggybacks. First, an analysis of the comparative costs of alternative treatments or healthcare programs is common to all forms of economic evaluation. Once the important and relevant costs are identified, they must be measured in appropriate physical and natural units 29 . However, our study found a high proportion of important and relevant costs missing from the included articles, which was strongly related to the manner in which costs were collected. Although various cost collection methods exist, a bottom-up method is usually used for individual-level economic evaluation 30 . When this method was used to calculate costs, one missing cost component led to missing total costs 31 . Second, less than 40% of the studies in this review performed sensitivity analyses for the treatment of missing data, although almost all the included studies had missing data. In reality, there will always be some uncertainty in the case of missing data, and it is difficult to have complete confidence in the results that rely on unobserved information 32 . Therefore, sensitivity analysis is particularly important as it can be a valuable tool to deal with the uncertainty caused by missingness and explore the impact of plausible alternative missing data assumptions on the economic evaluation 27 . It is precisely because of the ubiquity of missing data and the fact that the mechanisms of missing data are often not rigorously examined that sensitivity analyses are needed in future studies to compare the results under different hypotheses about the causes for the missing data, and different measurement process to demonstrate the robustness of the results 33 34 . Finally, the results of this study showed that the reported sample size of the economic evaluation was generally smaller than that of the clinical trials. However, previous studies indicated that the sample size required to detect significant differences in piggyback evaluations may be greater than that required to demonstrate effects 35–37 .Considering the missing data in the pRCT-based economic evaluation, the overall statistical power of the economic evaluation may be insufficient. Therefore, future economic evaluations of piggybacks should increase their sample size by fully considering the potential proportions of missing data. Further information on the sample size and statistical power of the pRCT-based economic evaluation will be reported elsewhere. Strengths and weaknesses of the study To the best of our knowledge, this is the first study to provide a comprehensive assessment of reporting and the extent of missing data in pRCTs-based economic evaluations. However, this study has several limitations. First, despite using comprehensive retrieval strategies in this study, the CONSORT Statement Extension checklist does not require adding the word “pragmatic” to the title or abstract 38 , potentially leading to missed eligible trials, thus introducing a possible selection bias and affecting the results. Second, since our study focused on the reporting quality, the methodological quality of handling missing data cannot be definitely concluded. For example, the scientific rigor and appropriateness of handling methods of missing data, including the quality of MI models, require further research. Finally, this study only included studies published in English, potentially limiting the scope of our findings and introducing a language bias. Conclusion The current descriptions and reporting of missing data in most studies are insufficient, and the high proportion of missing data in pRCT-based economic evaluations should be given more attention. There is room for enhancing the complete economic outcomes and improving the reliability of economic results. Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests to disclose. Funding: This research was supported by the Sichuan Science and Technology Program Science (2023NSFSC1046 (W.H.)), Ministry of Education of the People's Republic of China (22YJCZH065 (W.H.)), and the China Scholarship Council (No.202306210382 (J.H.)). Author Contribution Conceptualization: Wen Hui.Data curation: Yu Xin, Ruomeng Song, Jun Hao, Changjin Wu, Wentan Li, Ling Zuo, Xiyan Zhang, Wen Hui.Formal analysis: Yu Xin, Ruomeng Song, Wen Hui.Funding acquisition: Jun Hao, Wen Hui.Investigation: Yu Xin, Ruomeng Song, Jun Hao, Wen Hui.Methodology: Yu Xin, Ruomeng Song, Jun Hao, Wen Hui.Supervision: Yuanyi Cai, Huazhang WuValidation: Yu Xin, Jun Hao, Wen Hui.Visualization: Yu Xin, Wen HuiWriting– original draft: Yu Xin, Ruomeng Song.Writing– review & editing: Yu Xin, Wen Hui. Acknowledgements Not applicable. Availability of data and materials Full details are given in Appendix. References Little RJ, D'Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367:1355–60. Mallinckrodt CH, Sanger TM, Dube S, et al. Assessing and interpreting treatment effects in longitudinal clinical trials with missing data. Biol Psychiatry. 2003;53:754–60. Welsing PM, Oude RK, Collier S, et al. 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Supplementary Files SupplementaryFilesTableS1.docx Cite Share Download PDF Status: Published Journal Publication published 06 Mar, 2025 Read the published version in BMC Medical Research Methodology → Version 1 posted Editorial decision: Revision requested 01 Jan, 2025 Reviews received at journal 22 Dec, 2024 Reviewers agreed at journal 11 Dec, 2024 Reviews received at journal 12 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers invited by journal 28 Jun, 2024 Editor invited by journal 21 May, 2024 Editor assigned by journal 21 May, 2024 Submission checks completed at journal 21 May, 2024 First submitted to journal 16 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4429561","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":306066452,"identity":"a65b4e4f-28f5-4a8a-bda7-f7e62e50e0e6","order_by":0,"name":"Yu Xin","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Xin","suffix":""},{"id":306066453,"identity":"000891ad-b9cb-46cf-8c6b-e506bb4340a2","order_by":1,"name":"Ruomeng Song","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruomeng","middleName":"","lastName":"Song","suffix":""},{"id":306066454,"identity":"5cf3285a-8f23-4d7e-981d-40abd152f4b0","order_by":2,"name":"Jun Hao","email":"","orcid":"","institution":"National Clinical Research Centre for Cardiovascular Diseases, Peking Union Medical College and Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Hao","suffix":""},{"id":306066455,"identity":"9a129834-bbff-4684-8cda-0320437e94bc","order_by":3,"name":"Wentan Li","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wentan","middleName":"","lastName":"Li","suffix":""},{"id":306066456,"identity":"1918d173-cd63-42df-a089-4ffeba6293e3","order_by":4,"name":"Changjin Wu","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changjin","middleName":"","lastName":"Wu","suffix":""},{"id":306066457,"identity":"9d1c7d8a-3ad7-4f52-bf2e-232f5d7d16b3","order_by":5,"name":"Ling Zuo","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Zuo","suffix":""},{"id":306066458,"identity":"50c092c8-5061-4d61-abcb-4e90d339a9e6","order_by":6,"name":"Yuanyi Cai","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyi","middleName":"","lastName":"Cai","suffix":""},{"id":306066459,"identity":"7d32e5e6-5708-4750-9ee4-967d21a4b52a","order_by":7,"name":"Xiyan Zhang","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiyan","middleName":"","lastName":"Zhang","suffix":""},{"id":306066460,"identity":"20f65128-fcb5-42a8-bd21-d3d79901117a","order_by":8,"name":"Huazhang Wu","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huazhang","middleName":"","lastName":"Wu","suffix":""},{"id":306066461,"identity":"a9a07922-bea5-4182-886b-24c7ecded340","order_by":9,"name":"Wen Hui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACAwbGBiBlA+HxkKAljSQtYHCYBC3mEsltEh93nLfnn5HA+OBtG4O8OSEtljMS2yRnnrmdOONGArPh3DYGw50NhBx2I7HtNm/b7QQDiQQ2ad42hgSDA8Ro+dt2zh6ohf038VoY2w4wbgDawkycljMP23/2tiUnzjjzsFlyzjkJww0EtRxPf2zws83Onr89+eCHN2U28gRtQQLgOJUgXv0oGAWjYBSMAtwAAMDPQL2lje+9AAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Wen","middleName":"","lastName":"Hui","suffix":""}],"badges":[],"createdAt":"2024-05-16 08:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4429561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4429561/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12874-025-02519-z","type":"published","date":"2025-03-06T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57613862,"identity":"03b261f4-0d8b-4971-9933-85178ed5895f","added_by":"auto","created_at":"2024-06-03 10:56:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83420,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4429561/v1/9077077f933d42e0c3550ada.png"},{"id":78190857,"identity":"d2723e09-b1f5-4bfc-b7d8-7ab6165d2da1","added_by":"auto","created_at":"2025-03-10 19:51:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1453768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4429561/v1/a1dc8416-5767-491f-87e2-c9cb267ce952.pdf"},{"id":57614407,"identity":"beb64d33-3536-43ea-a8a2-3c88bcdfb425","added_by":"auto","created_at":"2024-06-03 11:04:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14779,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFilesTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4429561/v1/6fad54f02f3b9b51dd7f4a09.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Poor Reporting Quality and High Proportion of Missing Data in Economic Evaluations Alongside Pragmatic Trials: A Cross-sectional Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMissing data, which may lead to selection and information biases and jeopardize the validity, generalizability, and precision of study results, is one of the most frequent problems in clinical trials\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. In trial-based economic evaluations, the rate of data integrity in economic outcomes may be much lower than that in clinical outcomes. As previous trial-based economic evaluations have shown, randomized clinical trials may have data on 80\u0026ndash;95% complete cases of clinical outcomes\u003csup\u003e4\u0026ndash;7\u003c/sup\u003e, whereas only 50\u0026ndash;80% of randomized participants have complete economic data\u003csup\u003e8\u0026ndash;11\u003c/sup\u003e. The following reasons could explain why a high proportion of missing values occurred in trial-based economic evaluations. First, economic evaluations tend to collect rich, longitudinal information from individuals, such as their use of healthcare services and health-related quality of life, which usually consists of multiple questions to calculate total cost and health utility\u003csup\u003e12 13\u003c/sup\u003e. If one component question is missing, the total cost and health utility are also absent\u003csup\u003e14\u003c/sup\u003e. Therefore, missingness is more likely to occur in economic data. Second, economic variables may be considered less important by the researchers responsible for data collection, which could trigger higher rates of missingness\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePragmatic randomized controlled trials (pRCTs) measure the treatment effects in routine clinical practice, closely approximating the reality of the medical environment\u003csup\u003e16 17\u003c/sup\u003e. The characteristic features of pRCTs include (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) selecting clinically relevant alternative interventions for comparison, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) recruiting participants from heterogeneous practice settings, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) collecting data on a broad range of health outcomes, including patient-reported, global, and subjective outcomes\u003csup\u003e18 19\u003c/sup\u003e. Thus, well-conducted pRCT-based economic evaluations are more likely to generalize the results to an extended clinical setting and inform health-related decision-making. As the International Society for Pharmacoeconomics and Outcomes Research has proposed, pragmatic effectiveness trials are the best vehicles for economic studies\u003csup\u003e20\u003c/sup\u003e. However, one of the inevitable limitations of pragmatic design is that increasing non-adherence to the trial protocol, and even the use of electronic health records, results in inconsistent data collection and missing data\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e. Missing data may seriously compromise the credibility of causal inferences from pRCTs\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough several previous studies have revealed that missing data are very common in traditional trial-based economic evaluations, and the reporting quality is poor\u003csup\u003e12 23 24\u003c/sup\u003e, information on the proportion, handling approaches, and reporting quality of missing data in pRCT-based economic evaluations remains unknown. Therefore, the aim of this cross-sectional analysis was two-fold: First, to provide a comprehensive qualitative description of the extent of and handling approaches to missing economic outcomes in the included pRCT-based economic evaluations published over the last 10 years. Second, to provide a critical review of reporting and potential methodological issues in handling missing data to provide suggestions on how missing data should be reported for future research and update the methodological guidelines on handling missing data in pRCT-based economic evaluations, in accordance with the CONSORT-Outcome 2022 statement, the framework, and guidelines for the treatment of missing data in economic evaluations\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEligibility and exclusion criteria\u003c/h2\u003e \u003cp\u003eThis study included economic evaluations of pRCTs published in peer-reviewed journals. Abstracts, comments, letters, protocols, reviews, subgroup analyses, post-hoc analyses, pilot trials, and non-English language articles were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData source and search strategy\u003c/h2\u003e \u003cp\u003eA systematic literature search was performed using PubMed, Embase, the Cochrane Central Register of Controlled Trials, the Health Technology Assessment Database, and the National Health Service Economic Evaluation Database (NHS EED), covering the period from January 1, 2010, to December 31, 2021. On April 24, 2022, a complementary search was performed based on the inclusion and exclusion criteria. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provides the complete search strategy used to find articles of original research for inclusion in our systematic review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy selection and data extraction\u003c/h2\u003e \u003cp\u003eTitles and abstracts were screened by two sets of two independent reviewers (CW, WL, XZ, and WH). The same reviewers assessed the full texts of the identified studies for eligibility, and the reasons for exclusion were recorded. Discrepancies were resolved by consensus or by WH.\u003c/p\u003e \u003cp\u003eA structured questionnaire was developed based on WeChat and Wenjuanxing platforms to collect data from all eligible studies. Two reviewers (YX and RS) pilot-tested the predefined form to confirm its completeness, appropriateness, and applicability. Three teams of reviewers (YX, RS, JH, WL, CW, and LZ) independently collected the data from all eligible studies. Discrepancies were resolved by consensus or by WH. The following information was collected from each eligible study. The following basic information were extracted: sample size, follow-up time, country, Journal Impact Factor Quartile, and whether economy-related outcomes were the primary endpoints of each eligible study. Information on the occurrence of missing data, proportion of missing data, missing patterns, missing mechanisms, methods of handling missingness, imputation level, and relative information of multiple imputation (MI) were also extracted. Additionally, we obtained information on whether the results of the sensitivity analysis were consistent with the primary results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive analysis was conducted of all the variables. For continuous variables, data are presented as mean (standard deviation, SD) if normally distributed and otherwise as median [interquartile range, IQR] or median (range). For categorical variables, data are presented as frequencies divided by the total number and proportion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient and public involvement\u003c/h2\u003e \u003cp\u003eNo patients or members of the public were involved in setting the research question or the outcome measures, nor were they involved in developing plans for the design or implementation of the study or asked to advise on interpretation or writing up of results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSelected articles\u003c/h2\u003e \u003cp\u003eUsing this search strategy, a total of 715 articles were retrieved from the databases. After removing 234 duplicates, 481 studies were left for the first-round screening and 243 studies for full-text screening. Finally, 152 studies met the eligibility and exclusion criteria (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy characteristics of the included studies\u003c/h2\u003e \u003cp\u003eAmong the 152 included studies, the median sample size for economic evaluation was 343 (IQR, 202.5\u0026ndash;696), which was slightly smaller than the sample size in the protocol, which was 360 (IQR, 230\u0026ndash;750). The median follow-up period was 12 months (IQR, 6\u0026ndash;12). The UK (n\u0026thinsp;=\u0026thinsp;90, 59.21%) conducted the majority of the economic evaluations, followed by the Netherlands (n\u0026thinsp;=\u0026thinsp;24, 15.79%). More than 60% of the studies (n\u0026thinsp;=\u0026thinsp;98, 64.47%) were published in the top quartile of the Journal Impact Factor. About half of the studies (n\u0026thinsp;=\u0026thinsp;75, 49.34%) used economy-related outcomes as primary endpoints (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy characteristics of 152 included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncluded studies\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;152)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size for the protocol (median [IQR])\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360[230\u0026ndash;750]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size for economic evaluation (median [IQR])\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343[202.5\u0026ndash;696]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up (month) (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e (\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry (Top 5), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (59.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (15.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJIFQ, n (%)\u003c/b\u003e \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (64.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (24.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhether the economic-related outcomes were primary endpoints, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (49.34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (48.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e*\u003c/sup\u003e JIFQ: Journal Impact Factor Quartile. Reuters divides all journals into four equal categories based on their impact factors. The journals in Q1 were the highest ranked (top 25%) in a category, and the journals in Q4 were the lowest.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBasic information on missing data\u003c/h2\u003e \u003cp\u003eOf the 152 studies, approximately 75% of them included studies that missed cost and effect data simultaneously, whereas 25.66% did not report overall missingness. More specifically, missing effects were more common than missing costs (86.84% vs. 78.29%); however, the number of studies that did not mention missing costs was almost twice that of missing effects (21.05% vs. 12.50%). More than 50% (58/113) reported the proportion or quantity of overall missingness, and 64.71% and 54.55% of the studies reported missing costs and effects, respectively. The proportion of missingness of \u0026lt;\u0026thinsp;5% in the overall group was 3.45%, whereas the proportions of missing costs and effects were both lower than 10% (5.26% vs. 8.45%). In terms of the proportion of missing data, the overall missingness rate was 30.22% in 58 studies, whereas the median proportion of missing data was slightly higher than that of the missing effects (30.92% vs. 27.78%). According to the different lengths of the follow-up period, the median proportion of missing data in the\u0026gt;1-year group was higher than that of the \u0026le;\u0026thinsp;1-year group, particularly in the overall group (42.20 vs. 29.50%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic information on missing data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBasic information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNumber of studies, n/N (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. The occurrence of missing data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissingness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113/152 (74.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119/152 (78.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132/152 (86.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone missingness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/152 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1/152 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1/152 (0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39/152 (25.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32/152 (21.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19/152 (12.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Reporting the quantity of missing data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e58/113 (51.33)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e77/119 (64.71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e72/132 (54.55)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2.1 The proportion of missingness\u0026thinsp;\u0026lt;\u0026thinsp;5%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2/58 (3.45%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4/76 (5.26%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6/71 (8.45%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2.2 The proportion of missing data in the follow-up period\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median [IQR], %)\u003c/b\u003e \u003csup\u003e\u003cb\u003e\u0026amp;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e30.22[20.59\u0026ndash;4.67]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e30.92[15.68\u0026ndash;46.43]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e27.78[14.49\u0026ndash;39.80]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up \u0026le;\u0026thinsp;1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.50[19.95\u0026ndash;2.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.93[14.58\u0026ndash;46.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.12[14.28\u0026ndash;9.96]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up\u0026gt;1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.20[31.53\u0026ndash;1.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.72[19.71\u0026ndash;45.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.48[15.37\u0026ndash;67.43]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003e Overall, both cost and effect data are missing.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u0026amp;\u003c/sup\u003e The follow-up periods were the time points for calculating the incremental cost-effectiveness ratios.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDetails on dealing with missing data in the primary analyses\u003c/h2\u003e \u003cp\u003eAmong the studies with missing data, approximately one-fourth reported missing mechanisms, and only a few studies further reported the reasons for the missing mechanisms. Missing at random (MAR) was the most popular assumption of missingness, with a proportion of approximately 90%. Regarding specific methods for handling missing data, 27.43% of the studies simultaneously reported methods for handling missing costs and effects. However, the percentage of studies that separately reported the methods of dealing with costs or effects was approximately 80%. More specifically, MI was the most frequently used method (66.32% vs. 70.30%), followed by single imputation (20.62% vs. 16.83%) and deletion (19.59% vs. 12.87%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed information on handling missing data in primary analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNumber of studies, n/N (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Reporting missing mechanism\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/113 (27.43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25/119 (21.01)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31/132 (23.48)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27/31 (87.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23/25 (92.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28/31 (90.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3/31 (9.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1/25 (4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2/31 (6.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1/31 (3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1/25 (4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1/31 (3.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Reporting reasons for missing mechanism selection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5/31 (16.13)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5/25 (20.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5/31 (16.13)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Reporting methods of handling missingness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e31/113 (27.43)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e97/119 (81.51)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e101/132 (76.52)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e63/97 (66.32)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e71/101 (70.30)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSingle imputation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e20/97 (20.62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e17/101 (16.83)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean imputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14/20 (70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10/17 (58.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2/20 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4/17 (23.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression imputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2/20 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2/17 (11.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeletion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19/97 (19.59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13/101 (12.87)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18/19 (94.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12/13 (92.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1/19 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1/13 (7.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMAR, missing at random; MCAR, missing completely at random; MNAR, missing not at random; MI, multiple imputation; LOCF, last observation carried forward; CCA, complete case analysis; ACA, available case analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* One study assumed different mechanisms for the missing costs and effects.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDetails on dealing with missing data in the sensitivity analyses\u003c/h2\u003e \u003cp\u003eFifty-six (36.84%) studies conducted sensitivity analyses to address missing data. Of these studies, 12.50% reported missing mechanisms, and 83.93% reported handling methods. In contrast to the primary analyses, complete care analysis was the most popular method for dealing with missing data in the sensitivity analyses. The results of the sensitivity analysis were consistent with those of the primary analyses in 94.64% of the studies (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed information on sensitivity analysis for missing data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of studies, n/N (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Conducting sensitivity analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56/152 (36.84)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Reporting missing mechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/56 (12.50)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/7 (71.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/7 (28.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Handling methods of missing data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e47/56 (83.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28/47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16/47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle imputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Whether the results of the sensitivity analysis were consistent with the primary results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53/56 (94.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/56 (1.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/56 (3.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eMAR, missing at random; MCAR, missing completely at random; MNAR, missing not at random; CCA, complete case analysis; MI, multiple imputations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatement of principal findings\u003c/h2\u003e \u003cp\u003eThis study included a cross-sectional analysis of the reporting quality and quantity of missing data in pRCT-based economic evaluations published from 2010 to 2022 and found that the reporting quality of missing data in pRCT-based economic evaluations was insufficient. Although most studies claimed missing values, studies reporting a specific quantity of missing data accounted for approximately 60%. Additionally, the percentage was lower in studies with missing costs and effects. In terms of methods for handling missing data, the reporting quality in the primary analyses was higher than that in the sensitivity analyses.\u003c/p\u003e \u003cp\u003eFurthermore, missing data were obviously serious in pragmatic trial-based economic evaluations; nearly 90% of studies acknowledged missing data regardless of missing costs or effects, and less than 4% of studies had a missingness proportion of \u0026lt;\u0026thinsp;5%. The median percentage of missing data was approximately 30%, and the third quartile was 67.43%. Moreover, the longer the follow-up period, the higher the proportion of missing data, particularly in studies with missing costs and effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComparison to similar studies\u003c/h2\u003e \u003cp\u003eOver the past decade, missing data in piggyback economic evaluations have raised widespread concerns. Previous studies have found poorly reported and unclear methodologies for missing data in cost-effectiveness analyses alongside RCTs\u003csup\u003e14 27\u003c/sup\u003e. Moreover, recent analyses on the statistical quality of handling missing data in trial-based economic evaluations found that MI is the most common method for handling missing data under MAR mechanisms\u003csup\u003e28\u003c/sup\u003e, and some used only a single method without a sensitivity analysis and did not offer a justification of their approach of handling missing data\u003csup\u003e14\u003c/sup\u003e. These results are consistent with those of the present study. Our analysis extends previous studies by assessing reporting quality, the extent of missing data, and handling methods in pRCT-based economic evaluations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImplications for future research\u003c/h2\u003e \u003cp\u003eThe findings of this study have important implications for the future economic evaluation of piggybacks. First, an analysis of the comparative costs of alternative treatments or healthcare programs is common to all forms of economic evaluation. Once the important and relevant costs are identified, they must be measured in appropriate physical and natural units\u003csup\u003e29\u003c/sup\u003e. However, our study found a high proportion of important and relevant costs missing from the included articles, which was strongly related to the manner in which costs were collected. Although various cost collection methods exist, a bottom-up method is usually used for individual-level economic evaluation\u003csup\u003e30\u003c/sup\u003e. When this method was used to calculate costs, one missing cost component led to missing total costs\u003csup\u003e31\u003c/sup\u003e. Second, less than 40% of the studies in this review performed sensitivity analyses for the treatment of missing data, although almost all the included studies had missing data. In reality, there will always be some uncertainty in the case of missing data, and it is difficult to have complete confidence in the results that rely on unobserved information\u003csup\u003e32\u003c/sup\u003e. Therefore, sensitivity analysis is particularly important as it can be a valuable tool to deal with the uncertainty caused by missingness and explore the impact of plausible alternative missing data assumptions on the economic evaluation\u003csup\u003e27\u003c/sup\u003e. It is precisely because of the ubiquity of missing data and the fact that the mechanisms of missing data are often not rigorously examined that sensitivity analyses are needed in future studies to compare the results under different hypotheses about the causes for the missing data, and different measurement process to demonstrate the robustness of the results\u003csup\u003e33 34\u003c/sup\u003e. Finally, the results of this study showed that the reported sample size of the economic evaluation was generally smaller than that of the clinical trials. However, previous studies indicated that the sample size required to detect significant differences in piggyback evaluations may be greater than that required to demonstrate effects\u003csup\u003e35\u0026ndash;37\u003c/sup\u003e.Considering the missing data in the pRCT-based economic evaluation, the overall statistical power of the economic evaluation may be insufficient. Therefore, future economic evaluations of piggybacks should increase their sample size by fully considering the potential proportions of missing data. Further information on the sample size and statistical power of the pRCT-based economic evaluation will be reported elsewhere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and weaknesses of the study\u003c/h2\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to provide a comprehensive assessment of reporting and the extent of missing data in pRCTs-based economic evaluations. However, this study has several limitations. First, despite using comprehensive retrieval strategies in this study, the CONSORT Statement Extension checklist does not require adding the word \u0026ldquo;pragmatic\u0026rdquo; to the title or abstract \u003csup\u003e38\u003c/sup\u003e, potentially leading to missed eligible trials, thus introducing a possible selection bias and affecting the results. Second, since our study focused on the reporting quality, the methodological quality of handling missing data cannot be definitely concluded. For example, the scientific rigor and appropriateness of handling methods of missing data, including the quality of MI models, require further research. Finally, this study only included studies published in English, potentially limiting the scope of our findings and introducing a language bias.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current descriptions and reporting of missing data in most studies are insufficient, and the high proportion of missing data in pRCT-based economic evaluations should be given more attention. There is room for enhancing the complete economic outcomes and improving the reliability of economic results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was supported by the Sichuan Science and Technology Program Science (2023NSFSC1046 (W.H.)), Ministry of Education of the People's Republic of China (22YJCZH065 (W.H.)), and the China Scholarship Council (No.202306210382 (J.H.)).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Wen Hui.Data curation: Yu Xin, Ruomeng Song, Jun Hao, Changjin Wu, Wentan Li, Ling Zuo, Xiyan Zhang, Wen Hui.Formal analysis: Yu Xin, Ruomeng Song, Wen Hui.Funding acquisition: Jun Hao, Wen Hui.Investigation: Yu Xin, Ruomeng Song, Jun Hao, Wen Hui.Methodology: Yu Xin, Ruomeng Song, Jun Hao, Wen Hui.Supervision: Yuanyi Cai, Huazhang WuValidation: Yu Xin, Jun Hao, Wen Hui.Visualization: Yu Xin, Wen HuiWriting\u0026ndash; original draft: Yu Xin, Ruomeng Song.Writing\u0026ndash; review \u0026amp; editing: Yu Xin, Wen Hui.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eFull details are given in Appendix.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLittle RJ, D'Agostino R, Cohen ML, et al. 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BMJ-British Med J. 2000;321:1362\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrummond MF, Davies L. Economic analysis alongside clinical trials. Revisiting the methodological issues. Int J Technol Assess Health Care. 1991;7:561\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBackhouse ME. Use of randomised controlled trials for producing cost-effectiveness evidence: potential impact of design choices on sample size and study duration. PharmacoEconomics. 2002;20:1061\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZwarenstein M, Treweek S, Gagnier JJ, et al. Improving the reporting of pragmatic trials: an extension of the consort statement. BMJ-British Med J. 2008;337:a2390.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Economic evaluations, Pragmatic trials, Missing data, Cross-sectional survey","lastPublishedDoi":"10.21203/rs.3.rs-4429561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4429561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo assess the reporting quality of missing data in economic evaluations conducted alongside pragmatic randomized controlled trials (pRCTs).\u003c/p\u003e\u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eCross-sectional survey.\u003c/p\u003e\u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eData were extracted from PubMed and OVID (Embase, CENTRAL, HTA database, and NIH EED) from January 1, 2010, to April 24, 2022. Economic evaluations conducted with pRCTs were included and secondary analyses, abstracts, comments, letters, notes, editorials, protocols, subgroup analyses, pilot and feasibility trials, post-hoc analyses, and reviews were excluded. Two groups of two independent reviewers identified the relevant articles, and data were extracted from three groups of two reviewers.\u003c/p\u003e\u003ch2\u003eMain outcome measures\u003c/h2\u003e \u003cp\u003eDescriptive analyses were performed to assess characteristics of the included studies, missingness in the included studies, and handling of missing data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 715 studies were identified, of which 152 met the inclusion criteria. Overall, 113 articles reported missing data, 119 reported missing costs, and 132 reported missing effects. More than 50% (58/113) of the articles reported the proportion or quantity of overall missingness, and 64.71% and 54.55% reported missing costs and effects, respectively. The proportion of missingness of \u0026lt;\u0026thinsp;5% in the overall group was 3.45%, whereas the proportions of missing costs and effects were both lower than 10% (5.26% vs. 8.45%). In terms of the proportion of missing data, the overall missingness rate was 30.22% in 58 studies, whereas the median proportion of missing data was slightly higher than that of the missing effects (30.92% vs. 27.78%). For details on dealing with missing data, 56 (36.84%) studies conducted a sensitivity analysis on handling missing data. Of these studies, 12.50% reported missing mechanisms, and 83.93% examined handling methods.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eInsufficient description and reporting of missing data, along with a high proportion of missing data in pRCT-based economic evaluations, could decrease the reliability and extrapolation of conclusions, leading to misleading decision-making. Future research should include an increased sample size by fully considering the potential proportion of missing data and enhance the transparency and evidence quality of economic evaluation alongside pragmatic trials.\u003c/p\u003e","manuscriptTitle":"Poor Reporting Quality and High Proportion of Missing Data in Economic Evaluations Alongside Pragmatic Trials: A Cross-sectional Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 10:56:06","doi":"10.21203/rs.3.rs-4429561/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-01T12:35:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-22T22:27:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325395821367842163174321718965236549627","date":"2024-12-11T16:55:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-12T21:06:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182261157763112337399126368192918371008","date":"2024-07-05T09:12:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66833347641476558895172657030966291577","date":"2024-07-01T17:34:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-28T08:32:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-21T18:01:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-21T17:59:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-21T17:59:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2024-05-16T08:22:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96dc4c90-8128-4d20-8a9c-cca8fd3942ea","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T19:49:32+00:00","versionOfRecord":{"articleIdentity":"rs-4429561","link":"https://doi.org/10.1186/s12874-025-02519-z","journal":{"identity":"bmc-medical-research-methodology","isVorOnly":false,"title":"BMC Medical Research Methodology"},"publishedOn":"2025-03-06 15:56:58","publishedOnDateReadable":"March 6th, 2025"},"versionCreatedAt":"2024-06-03 10:56:06","video":"","vorDoi":"10.1186/s12874-025-02519-z","vorDoiUrl":"https://doi.org/10.1186/s12874-025-02519-z","workflowStages":[]},"version":"v1","identity":"rs-4429561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4429561","identity":"rs-4429561","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}