Synthetic control arm from mixed clinical trials and real-world data from LYSA group for untreated diffuse large B cell lymphoma patients aged over 80 years: a bona fide strategy for innovative clinical trials

Synthetic control arm from mixed clinical trials and real-world data from LYSA group for untreated diffuse large B cell lymphoma patients aged over 80 years: a bona fide strategy for innovative clinical trials | 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 Article Synthetic control arm from mixed clinical trials and real-world data from LYSA group for untreated diffuse large B cell lymphoma patients aged over 80 years: a bona fide strategy for innovative clinical trials Benoit Tessoulin, Valentin LETAILLEUR, Isabelle Chaillol, Fanny Cherblanc, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6822758/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Blood Cancer Journal → Version 1 posted 10 You are reading this latest preprint version Abstract Patients over 80 years (≥ 80 y.o) with newly diagnosed diffuse large B-cell lymphomas (DLBCL) are underrepresented in clinical trials (CT). Use of synthetic control arms (SCA) could help to build innovative comparatives trials. With this analysis, we aim to demonstrate the clinical performance & use of such SCA. With data from both CT (LNH09-7B) and real-world (REALYSA), we built a mixed SCA composed of ≥ 80 y.o patients with DLBCL in first line of treatment. In order to display clinically meaningful results, we demonstrate how we can reproduce SENIOR results, a double-arm randomized CT (RCT), by switching the internal control arm by our newly built SCA. Patients between arms were balanced using stabilized inverse probability of treatment weighting approach based on propensity scores (PS) and the endpoint was overall survival (OS). All covariates included in PS were well balanced after weighting, and OS of Mixed SCA vs. SENIOR experimental arm were not statistically different, with a HR of 0.743 [0.494–1.118] (p = 0.1654). Use of SCA built only from real-world data (REALYSA) and sensitivity analyses using different missing data management methods didn’t differ from the whole analysis. In newly diagnosed elderly DLBCL patients, the use of SCAs can mimic the control arm of a RCT and could be used to build comparative CT for elderly patients and come-up with fast-innovative CTs. Health sciences/Medical research/Clinical trial design/Clinical trials Health sciences/Diseases/Haematological diseases/Haematological cancer/Lymphoma/Non-hodgkin lymphoma/B-cell lymphoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Diffuse large B cell lymphoma (DLBCL) is the most common lymphoid malignancy among elderly, and about 25% ( 1 ) of new diagnoses concern patients aged 80 years or older (≥ 80y.o). Besides obvious differences related to treatment tolerance and management compared to the younger population, raising knowledge, in molecular cell of origin subclassification, for example, demonstrates that DLBCL of the elderly may have specific aspects ( 2 , 3 ). Yet, they are underrepresented in clinical trials, and few trials focus on very elderly patients ( 1 ). Recommended treatment in first line setting, as established by two phase II clinical trials, LNH03-7B ( 4 ) and LNH09-7B ( 5 ), is Rituximab and reduced CHOP (Cyclophosphamide, Doxorubicine, Vincristine, Prednisone) combination (R-miniCHOP), with a 2-year overall survival (OS) approaching 60%. Alternatively, regimens replacing or omitting anthracyclines can be proposed for frail patients or those with cardiac function impairment. ( 6 – 8 ). Pre-phase treatment, combining oral prednisone with or without vincristine and cyclophosphamide, is recommended for the elderly and should be considered after 80 years-old, as it has been shown to be beneficial for performans status improvement, allowing better tolerance during subsequent R-miniCHOP treatment ( 5 , 9 , 10 ). More than a decade after LNH09-7B, improving 1st line treatment outcomes remains tricky in this population, despite recent overall advances brought by targeted therapies and immunomodulatory agents. Indeed, although the lenalidomide and R-miniCHOP combination (R²miniCHOP) had an appealing rational, as the activated B-cell (ABC) DLBCL subtype increases with aging ( 2 ), SENIOR ( 11 ), the first -and only published to date- randomized phase III clinical trial (RCT) focused on ≥ 80 y.o patients, failed to show an improvement in OS, the primary endpoint, which can be partly explained by toxicity issues. Recruitment of the ≥ 80 y.o. in clinical trials can be more challenging than for the younger patients, explaining partly the lack of focused RCT. New combinations are in evaluation in ongoing trials ( 12 ), including RCTs ( 13 , 14 ), that will hopefully bring new treatment options with hypothetical approvals. At a lower level of proof, phase II clinical trials can bring new opportunities in 1st line management. However, comparing results from different phase II trials is not recommended as variations in patient populations, study design or endpoints can lead to misleading conclusions. Use of synthetic control arms (SCA) in clinical trials with innovative designs could improve statistical confidence, replacing either entirely the internal control arm (analysis of a phase II CT), or partially (phase III RCT with a mixed control arm composed of randomized patients and historical patients) ( 15 – 17 ). Even if DLBCL ≥ 80 y.o cannot be considered as a rare disease, the lack of representation and recruitment issues in clinical trials can justify the use of SCA, with rigorous statistical methods to control covariates balance between arms and to reduce biases related to the lack of randomization ( 18 – 22 ) and general rules, as defined by agencies, have been published ( 23 , 24 ). In this study, we aimed to build a SCA from mixed real-world and clinical trial data for ≥ 80 y.o DLBCL patients at first line of treatment, and to validate its clinical and statistical relevance by applying it to the SENIOR trial, the only published RCT in this population. Material and Methods Patients and study design Patient-level data from REALYSA ( 25 ) and LNH09-7B ( 5 ) databases were retrieved for SCA patients, and data from SENIOR trial ( 8 ) were used for validation. REALYSA is a French real-world multicentric observational cohort recruiting newly diagnosed lymphoma patients since November 2018. LNH09-7B was a phase II clinical trial assessing efficacy of Ofatumumab and miniCHOP preceded by a pre-phase treatment (vincristine and oral prednisone) as first line treatment of DLBCL patients ≥ 80 y.o, which resulted in a 2-years OS of 64.7% [95% CI: 55.3–72.7]. SENIOR trial was a phase III RCT assessing RminiCHOP vs. R-Lenalidomide-miniCHOP, with a pre-phase treatment, as first line treatment of DLBCL patients ≥ 80 y.o, with similar inclusion criteria as LNH09-7B. This study failed to show an improvement in OS with the addition of lenalidomide (2-years OS of 66% for RminiCHOP versus 65.7% for R²miniCHOP arm). Patients from LNH09-7B had their treatment started from June 2010 to November 2011, and those from SENIOR from October 2014 to September 2017. From REALYSA cohort, we included patients aged ≥ 80 y.o., treated with RminiCHOP combination as first line treatment, included in the cohort from December 2018 to 31 December 2021. Patients with performans status (ECOG) of 3 or 4 and Ann Arbor stage I were not included to match SENIOR inclusion criteria. Data were exported from the registry the 25th of June 2024. Clinical trials patients with non-compliant diagnoses (other than DLBCL or high-grade B cell lymphoma (HGBL)) according to anatomopathological centralized reviews realized in each trial or local diagnosis for REALYSA were excluded. The study was designed as follows: first step was to build a mixed SCA (“Mixed SCA 1”) from real-world data (REALYSA) and clinical trial data (LNH09-7B) adjusted on SENIOR control arm and to evaluate if it could mimic the internal control arm from SENIOR. Second step was to build a mixed SCA (“Mixed SCA 2”) adjusted on experimental arm of SENIOR, to evaluate if we could replicate the efficacy results from SENIOR study by switching the internal control arm by the Mixed SCA 2. For each step, populations were balanced as described below. Primary and only endpoint for arms comparison after weighting was OS, measured from date of inclusion (or date of diagnosis in REALYSA) to date of death. As a high number of REALYSA patients had a limited follow-up period, we chose to censor all patients at 24 months for all cohorts. Matching procedures: Propensity scores (PS) were estimated for each patient included using logistic regression with following covariates : sex, age (spline), Ann Arbor stage (I-II versus III-IV), Performans Status (ECOG) (0 vs. 1 vs. 2), number of extranodal sites involved ( 10cm, albumin level in gram per litter (g/L) (spline). For comparison between the Mixed SCA 1 and SENIOR internal control arm, PS allows to estimate the probability for a patient to receive “RminiCHOP via SENIOR”. For comparison between the Mixed SCA 2 versus SENIOR experimental arm, PS allows to estimate the probability for a patient to receive R²miniCHOP. Then, patients’ covariates between arms were weighted using the stabilized inverse probability of treatment weighting (sIPTW) statistical approach, the probability of treatment being reflected by PS ( 26 ). To avoid positivity violations, patients with extreme PS (below 0.1 or over 0.9) were excluded ( 27 ). After matching procedures, standardized mean differences (SMD) were estimated between arms for each covariate included for PS estimation to check balance in covariates’ distributions. With the statistical approach used, sIPTW allowed to estimate the average treatment effect (ATE). Missing data management: Due to the high number of missing data for patients from REALYSA for some covariates, multiple imputation method “across” was performed (15 imputations by patient) ( 28 , 29 ). That generated 15 complete datasets. PS were calculated for each of the datasets. Then, for each patient, the median of the 15 PS was used for weighting. The SMD presented in this manuscript were calculated on real data, i.e. before imputation. With the “across” missing management method, outcome analyses are performed on one dataset. Statistics for outcome analyses: As first objective was to assess if the Mixed SCA 1 could mimic the internal control arm of SENIOR, Hazard Ratios (HR) 95% confidence intervals were expected to include 1. The second objective was to reproduce SENIOR results by replacing the internal control arm by the Mixed SCA 2, HR were expected to overlap with the results obtained in SENIOR trial (HR [95%CI] of 0.996 [0.66–1.51]), including 1 in the HR 95%CI. OS curves were generated using Kaplan-Meier method, and OS curves were compared with log-rank test. HR and 95% confidence intervals (CI) were calculated using Cox proportional hazards model. One-sided p-value below 0.05 was considered significant. Analyses were performed using SAS software 9.4. Sensitivity analyses: Sensitivity analyses were performed using data coming only from REALYSA cohort to evaluate the possibility to build a well-balanced SCA using only real-world data (REALYSA-SCA), with the same methodology used for the mixed SCA building. Additionally, we performed sensitivity analysis using different missing data management methods on Mixed SCAs analyses or REALYSA-SCA analyses. “Complete cases” method excludes patients with missing data. For “within” method, analyses are performed on each imputed dataset (15 analyses) and results were combined using Rubin’s rules ( 28 , 29 ). Results Patients’ characteristics: Overall, in this study, we included 73 patients from LNH09-7B and 97 patients from REALYSA to source mixed SCAs, and 98 or 104 patients from SENIOR standard or experimental arm, respectively, to assess Mixed SCAs relevance (Fig. 1 ). This population will be referred as confirmed Diagnosis Set. All included patients received anti-CD20 monoclonal antibody (mAb) + miniCHOP based regimen as first line treatment. Table I shows characteristics of patients from each cohort included in confirmed Diagnosis Set. Most patients included in this study had an intermediary-high or high risk DLBCL (77.1% of overall patients with IPI score at 3 or higher). PS were calculated for all patients before each sIPTW weighting procedure. Five patients were excluded (Based on Mixed-SCA 1 PS: 2 from SENIOR standard arm, and based on Mixed-SCA 2: 1 from SENIOR experimental arm and 2 from Mixed-SCA 2) because of extreme values (above 0.9 or below 0.1) (Fig. 1 ). Final population was included in the “PS set”. (Fig. 1 ) In the PS set, some covariates were unbalanced before weighting, with an absolute SMD above 0.1 for 4 covariates (Sex, Performans Status 0 and 2, Mass > 10cm and Ann Arbor stage) before sIPTW between SENIOR Standard arm and Mixed SCA 1 and for 5 covariates (Sex, Mass > 10cm, LDH, IPI, B symptoms) between SENIOR experimental arm and Mixed SCA (Fig. 2 ). SMD were also calculated on a pool of the 15 datasets with imputation and were similar. Weighting procedures: Final populations in the PS set were weighted with the sIPTW method: SENIOR Standard arm with Mixed SCA 1 and SENIOR Experimental arm with Mixed SCA 2. Table II shows patients’ characteristics distribution in the 4 arms before and after weighting. Using the “across” method to manage missing data, weighting procedures with sIPTW were efficient to balance covariates between arms as all SMD for covariates included in the PS were below 0.10 (Fig. 2 ). To explore if the missing data management method used in this study could impact weighting efficiency, we also analyzed balance of covariates using the “complete case” or “within” method. SMD for all covariates were < 0.1 whatever the method used for missing data management. Moreover, in the “within” method, at different imputations (from 1 to 15), SMD for all covariates were < 0.1 and variation of SMD values across the different imputations was very low ( Supplemental Fig. 1 ). Outcome analysis: First step was to assess if Mixed SCA 1 could reproduce SENIOR control arm’s OS. OS was not significantly different between SENIOR control arm and Mixed SCA 1, with an HR [95%CI] of 0.86 [0.57–1.32] (p = 0.493) before weighting, and an HR of 0.79 [0.52–1.20] (p = 0.277) after weighting with sIPTW (Fig. 3 , A B). Second step was to assess the reproducibility of SENIOR trial results by switching SENIOR control arm by Mixed SCA 2. In our study, OS was not significantly different between SENIOR experimental arm and Mixed SCA 2 with an HR of 0.83 [0.55–1.25] (p = 0.3631) before weighting, and an HR of 0.74 [0.49–1.12] (p = 0.165) after weighting with sIPTW (Fig. 3 , C D ). There was no statistically significant difference in OS between SENIOR arms and Mixed SCAs using different missing data management methods ( Supplemental Table I ). REALYSA-SCA: We also assessed feasibility and viability of SCAs (REALYSA-SCAs) built with real-world data from REALYSA cohort only, with the same methods as the Mixed SCAs. Tables III shows patients’ characteristics from SENIOR control arm or experimental arm compared to patients from REALYSA-SCAs before and after sIPTW. More covariates were imbalanced before weighting compared to analyses conducted with Mixed SCAs, but after weighting, SMDs of all covariates were < 0.1 (Fig. 4 ). Weighting procedures were efficient, irrespective of the weighting method or the missing data management method used ( Supplemental Fig. 2 ). OS was not significantly different between SENIOR control arm and REALYSA-SCA-1 with an HR [95%CI] of 0.89 [0.56–1.44] (p = 0.640) before weighting and an HR of 0.90 [0.56–1.43] (p = 0.478) after sIPTW. Similarly, OS was not significantly different between SENIOR experimental arm and REALYSA-SCA-2 with an HR of 0.84 [0.53–1.35] (p = 0.474) before weighting and an HR of 0.88 [0.55–1.41] (p = 0.612) after weighting with sIPTW (Fig. 5 ,A-D). OS comparison results between REALYSA-SCAs and SENIOR standard or experimental arm obtained in sensitivity analyses were close to those obtained with the main methodology, and no statistically significant difference was observed ( Supplemental Table I ). Discussion In this study, we showed that a well-balanced synthetic control arm (SCA) designed for ≥ 80y.o DLBCL patients in the first line setting, with data extracted from historical clinical trial and real-world settings, could mimic the internal control arm of a RCT. Switching the internal control arm by a SCA reproduced efficacy results based on OS comparison, with an HR [95%IC] of 0.743 [0.494–1.118] compared to an HR of 0.996 [0.66–1.51] in SENIOR trial. This research demonstrates that we should open the field of newly-designed trials to improve our clinical-trial accrual and overall clinical benefit, even in the very elderly setting. Our selected balancing procedure with sIPTW method was highly efficient with all SMD < 0.1 for covariates included in the Propensity Score calculation along with a low drop-rate of “extreme patients’ (0,7 to 1,4% (Fig. 1 )), ensuring robustness of the results. We used mixed sources of real-world data and clinical trial data to build this mixed SCA, to strengthen the extent of reliability of our results in a perspective of "real-life" incoming phase III clinical trials. Surprisingly, we observed a limited number of real-world-sourced patients who would be excluded after applying inclusion criteria ( Performans status I). Regarding patients’ characteristics, those results showed that ≥ 80y.o patients with DLBCL in first line setting are quite similar despite very different inclusion periods between LNH09-7B, SENIOR and REALYSA. Indeed, most patients’ characteristics were already balanced before matching as only few covariates had a high SMD despite the absence of randomization and the use of real-world data. It suggests that 1) by applying SENIOR trial population inclusion criteria we selected a substantially homogeneous population of DLBCL elderly patients to build SCAs and 2) SENIOR trial enrolled an unbiased DLBCL elderly population with immuno-chemotherapy intent of treatment. Of note, analysis based on a SCA only built with these real-world data showed that patients could also be well balanced with SENIOR patients from both internal control arm or experimental arm with the sIPTW method (REALYSA-SCA) with an HR [95%CI] of 0.895 [0.560–1.429] and 0.877 [0.547–1.407] respectively. Some limitations can be taken into account for our study. Period of data accrual is different between LNH09-7B trial data (2010 to 2011), and REALYSA (2018 to 2021), and we cannot rule out that supportive care may have improved during this 10y time-frame. Furthermore, we couldn’t incorporate prognostic geriatric assessments ( e.g. Instrumental Activities of Daily Living (IADL) scale) into the PS calculation, because of the lack of this information in all datasets. In our dataset, data were prospectively collected, with a low rate of missing data except for two covariates (mass > 10cm and albumin level with > 10% of missing data from REALYSA patients) ( 30 ). Moreover, we showed that missing data could be reliably imputed, allowing us to keep steady sample size with robust outcome analyses results. Indeed, sensitivity analyses for missing data management with “complete cases”, or “within” multiple imputation methods led to same conclusions as for the main (“across”) method. From a statistical point-of-view, we chose sIPTW weighting method because it resulted in a pseudo-population with stable and comparable sample sizes, as opposed to IPTW which would have here resulted in a larger pseudo-population (that may result in an increased rate of type 1 error). As illustrated in sensitivity analyses, CI were narrower with IPTW compared to sIPTW matching, but we still obtained consistent results with IPTW. Other examples of SCA building and validation emerged recently in hematology field using data at an individual level from historical clinical trials or real-world data with PS-based methods to control for confounding variables ( 31 , 32 ). For example, another study also used REALYSA data to build a SCA with newly diagnosed patients with advanced Hodgkin lymphoma and reproduced efficacy results of the phase III RCT AHL2011 ( 33 ). We believe our study demonstrates that in a difficult to study population, SCA could be used as a bona- fide strategy to improve very elderly DLBCL patient’s outcomes by allowing trials with “ in silico” control arms along with more promising experimental arms. Indeed, SENIOR is currently the only published RCT in this setting and recruited 249 patients in three years within 100 centers in France and Belgium, illustrating the challenges to enroll these patients. The mixed SCAs we built could be used to help assessment of new combinations with targeted therapies that could improve OS compared to RminiCHOP based regimen, for which the adjunction of another drug seems tricky due to toxicity issues in this population. In our opinion, OS is the most relevant primary outcome to compare SCA to another arm, especially in the elderly population, as first progression is soon followed by death in this low-reserve population. Furthermore, health authorities are now giving weight to these indirect statistically sound strategies to allow market access to new treatments (if the control arm is the right one), and we could increase this weight by new design of trials. For instance, comparative trials with control arms composed of 50% of external patients and 50% of randomized patients (in order to keep toxicity comparisons available) could accelerate accrual time by reducing the number of patients to include, or, with a steady number of patients to include, allow more patients to be included in the experimental arm (2:1 randomization). To conclude, we built here a SCA from mixed clinical trial and real-world data for ≥ 80 y.o DLBCL patients in first line setting that could be used to build comparative trials with an innovative design, that could help to explore the ability of new treatment combinations to improve survival in this otherwise poorly-represented population in clinical trials. Declarations Author contributions VL, BT, IC & FC designed, coordinated the research, analyzed, interpreted the data, and wrote the manuscript. Acknowledgments The authors thank all the patients and their families for their confidence, as well as the LYSA investigators and the CRA teams, LYSARC staff, the Hospices Civils de Lyon (sponsor of REALYSA study). The initial studies (SENIOR, LNH09-7B and REALYSA) were financially supported by Roche, Takeda, Janssen, Amgen, Celgene-Bristol Myers Squibb, Astra-Zeneca, Abbvie and GSK. References Kanapuru B, Singh H, Kwitkowski V, Blumenthal G, Farrell AT, Pazdur R. Older adults in hematologic malignancy trials: Representation, barriers to participation and strategies for addressing underrepresentation. Blood Reviews. 2020;43:100670. Mareschal S, Lanic H, Ruminy P, Bastard C, Tilly H, Jardin F. The proportion of activated B-cell like subtype among de novo diffuse large B-cell lymphoma increases with age. Haematologica. 2011;96(12):1888–90. Wilson WH, Wright G, Huang DW, Hodkinson B, Balasubramanian S, Fan Y, et al. Effect of Ibrutinib with R-CHOP Chemotherapy in Genetic Subtypes of DLBCL. Cancer Cell. 2021;39(12):1643–1653.e3. Peyrade F, Jardin F, Thieblemont C, Thyss A, Emile JF, Castaigne S, et al. Attenuated immunochemotherapy regimen (R-miniCHOP) in elderly patients older than 80 years with diffuse large B-cell lymphoma: a multicentre, single-arm, phase 2 trial. The Lancet Oncology. 2011;12(5):460–8. Peyrade F, Bologna S, Delwail V, Emile JF, Pascal L, Fermé C, et al. Combination of ofatumumab and reduced-dose CHOP for diffuse large B-cell lymphomas in patients aged 80 years or older: an open-label, multicentre, single-arm, phase 2 trial from the LYSA group. The Lancet Haematology. 2017;4(1):e46–55. Moccia AA, Schaff K, Freeman C, Hoskins PJ, Klasa RJ, Savage KJ, et al. Long-term outcomes of R-CEOP show curative potential in patients with DLBCL and a contraindication to anthracyclines. Blood Advances. 2021;5(5):1483–9. Fields PA, Townsend W, Webb A, Counsell N, Pocock C, Smith P, et al. De Novo Treatment of Diffuse Large B-Cell Lymphoma With Rituximab, Cyclophosphamide, Vincristine, Gemcitabine, and Prednisolone in Patients With Cardiac Comorbidity: A United Kingdom National Cancer Research Institute Trial. JCO. 2014;32(4):282–7. Laribi K, Denizon N, Bolle D, Truong C, Besançon A, Sandrini J, et al. R-CVP regimen is active in frail elderly patients aged 80 or over with diffuse large B cell lymphoma. Ann Hematol. 2016;95(10):1705–14. Pfreundschuh M, Schubert J, Ziepert M, Schmits R, Mohren M, Lengfelder E, et al. Six versus eight cycles of bi-weekly CHOP-14 with or without rituximab in elderly patients with aggressive CD20 + B-cell lymphomas: a randomised controlled trial (RICOVER-60). The Lancet Oncology. 2008;9(2):105–16. Lugtenburg PJ, Mutsaers PGNJ. How I treat Elderly Patients with DLBCL in the frontline setting. Blood. 2022;blood.2020008239. Oberic L, Peyrade F, Puyade M, Bonnet C, Dartigues-Cuillères P, Fabiani B, et al. Subcutaneous Rituximab-MiniCHOP Compared With Subcutaneous Rituximab-MiniCHOP Plus Lenalidomide in Diffuse Large B-Cell Lymphoma for Patients Age 80 Years or Older. JCO. 2021;39(11):1203–13. Tessoulin B, Depaus J, Feugier P, Fouillet L, Abraham J, Amorin S, et al. Verlen, “Very Elderly Rituximab Associated to Lenalidomide - Tafasitamab Combination in Frontline DLBCL Patients”, a Phase II Open-Label Study Evaluating Efficacy of Lenalinomide and Tafasitamab Combination Associated to Rituximab in Frontline Diffuse Large B-Cell Lymphoma Patients of 80 y/o or Older from the Lysa Group. Blood. 2023;142(Supplement 1):3094. Jerkeman M, Leppä S, Hamfjord J, Brown P, Ekberg S, José María Ferreri A. S227: INITIAL SAFETY DATA FROM THE PHASE 3 POLAR BEAR TRIAL IN ELDERLY OR FRAIL PATIENTS WITH DIFFUSE LARGE CELL LYMPHOMA, COMPARING R-POLA-MINI-CHP AND R-MINI-CHOP. Hemasphere. 2023;7(Suppl):e91359ec. Brem EA, Li H, Beaven AW, Caimi PF, Cerchietti L, Alizadeh A, et al. SWOG 1918: A Phase II/III randomized study of R-miniCHOP with or without oral azacitidine (CC-486) in participants age 75 years or older with newly diagnosed aggressive non-Hodgkin lymphomas - Aiming to improve therapy, outcomes, and validate a prospective frailty tool. J Geriatr Oncol. 2022;13(2):258–64. Thorlund K, Dron L, Park JJH, Mills EJ. Synthetic and External Controls in Clinical Trials – A Primer for Researchers. Clin Epidemiol. 2020;12:457–67. Lambert J, Lengliné E, Porcher R, Thiébaut R, Zohar S, Chevret S. Enriching single-arm clinical trials with external controls: possibilities and pitfalls. Blood Adv. 2022;7(19):5680–90. Cucherat M, Laporte S, Delaitre O, Behier JM, d’Andon A, Binlich F, et al. From single-arm studies to externally controlled studies. Methodological considerations and guidelines. Therapies. 2020;75(1):21–7. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–107. Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med. 2014;33(7):1242–58. Allan V, Ramagopalan SV, Mardekian J, Jenkins A, Li X, Pan X, et al. Propensity score matching and inverse probability of treatment weighting to address confounding by indication in comparative effectiveness research of oral anticoagulants. J Comp Eff Res. 2020;9(9):603–14. Schulte PJ, Mascha EJ. Propensity Score Methods: Theory and Practice for Anesthesia Research. Anesthesia & Analgesia. 2018;127(4):1074. Xie J, Liu C. Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data. Stat Med. 2005;24(20):3089–110. Motrinchuk AS, Belousov DYu. Considerations for the design and conduct of externally controlled trials for drug and biological products. MyRWD. 2023;3(4):29–40. Abraham J. International Conference On Harmonisation Of Technical Requirements For Registration Of Pharmaceuticals For Human Use. In: Tietje C, Brouder A, editors. Handbook of Transnational Economic Governance Regimes [Internet]. Brill | Nijhoff; 2010 [cited 2024 Sep 2]. p. 1041–53. Available from: https://brill.com/view/book/edcoll/9789004181564/Bej.9789004163300.i-1081_085.xml Ghesquières H, Rossi C, Cherblanc F, Le Guyader-Peyrou S, Bijou F, Sujobert P et al. A French multicentric prospective prognostic cohort with epidemiological, clinical, biological and treatment information to improve knowledge on lymphoma patients: study protocol of the "REal world dAta in LYmphoma and survival in adults" (REALYSA) cohort. BMC Public Health. 2021;21(1):432. Xu S, Ross C, Raebel MA, Shetterly S, Blanchette C, Smith D. Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals. Value Health. 2010;13(2):273–7. Shiba K, Kawahara T. Using Propensity Scores for Causal Inference: Pitfalls and Tips. J Epidemiol. 2021;31(8):457–63. Leyrat C, Seaman SR, White IR, Douglas I, Smeeth L, Kim J, et al. Propensity score analysis with partially observed covariates: How should multiple imputation be used? Stat Methods Med Res. 2019;28(1):3–19. Nguyen TQ, Stuart EA. Multiple imputation for propensity score analysis with covariates missing at random: some clarity on “within” and “across” methods. American Journal of Epidemiology. 2024;193(10):1470–6. Ghesquières H, Cherblanc F, Belot A, Micon S, Bouabdallah KK, Esnault C, et al. Challenges for quality and utilization of real-world data for diffuse large B-cell lymphoma in REALYSA, a LYSA cohort. Blood Adv. 2024;8(2):296–308. Yin X, Stuart E, Burcu M, Stewart M, Lamont E, Davi R. The Validity of a Synthetic Control Arm Derived from Historical Multiple Myeloma Clinical Trials. Blood. 2023;142(Supplement 1):6628. Van Le H, Van Naarden Braun K, Nowakowski GS, Sermer D, Radford J, Townsend W, et al. Use of a real-world synthetic control arm for direct comparison of lisocabtagene maraleucel and conventional therapy in relapsed/refractory large B-cell lymphoma. Leuk Lymphoma. 2023;64(3):573–85. Rossi C, Marouf A, Deau Fischer B, Cherblanc F, Cugnod E, Chartier L, et al. First Line Therapy Evaluation Using Propensity Score Approach in Newly Diagnosed Advanced Classical Hodgkin Lymphoma Patients from Prospective Real-World Realysa Cohort and Phase 3 AHL2011 Trial. Blood. 2023;142:3056. Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Table1.xlsx Table I: Patients’ characteristics from the confirmed diagnosis set Characteristics of all patients included, from their cohort of origin are shown, before propensity score assessment and weighting procedures. Values are displayed as n(%), or mean(SD) when specified. SD: standard deviation; IPI: international prognostic index; LDH: lactate dehydrogenase; HGBL: high grade B-cell lymphoma; DLBCL: diffuse large B-cell lymphoma. Table2.xlsx Table II: Patients’ characteristics from SENIOR standard or SENIOR experimental arm and Mixed SCA 1 or 2 from the PS set before and after sIPTW weighting Table3.xlsx Table III: Patients’ characteristics from SENIOR standard or SENIOR experimental arm and REALYSA-SCA from the PS set, before and after sIPTW weighting SupplementalTable1.xlsx Supplemental Table I: HRs with 95% CI of OS comparison between SENIOR standard or experimental arm and Mixed-SCAs or REALYSA-SCAs, depending on missing data management method (across, complete case or within) used. SupplementalFigure1.tif Supplemental Figure 1: Balance between Mixed SCAs and (A) SENIOR control arm or (B) SENIOR experimental arm: SMD for each covariate after sIPTW using "within" missing management method according to different imputations SupplementalFigure2.tif Supplemental Figure 2: Balance between REALYSA-SCAs and (A) SENIOR control arm or (B) SENIOR experimental arm: SMD for each covariate after sIPTW using "within" missing management method according to different imputations Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Blood Cancer Journal → Version 1 posted Editorial decision: revise 08 Jul, 2025 Review # 1 received at journal 03 Jul, 2025 Review # 2 received at journal 18 Jun, 2025 Reviewer # 2 agreed at journal 18 Jun, 2025 Reviewer # 1 agreed at journal 13 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Submission checks completed at journal 10 Jun, 2025 First submitted to journal 09 Jun, 2025 Unknown event 05 Jun, 2025 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-6822758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469795043,"identity":"92b843e1-275b-49fd-b8dd-eaac6ca038d3","order_by":0,"name":"Benoit Tessoulin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJklEQVRIie3QsUrDQBjA8S8UzHKQ9SsdfIULB2kLxb5KjkKnIwhdCkJ1SpaUrhl8CEEQB4cUwSynrtlMEOrSoWMXwUsatJBEV4f7ExLuwu/uEgCd7h9Gi5v7PTxHsIAcvZ+3ERewGiJ0rypSPmQLgR+irvgP0jeX6yzfw8Kykvyd0IHHkucnNO7PvLEZZLB+qJFh+DKhxcG6kWCMUJw50puiISczQiSFeFM/WCocLAhNyUlPEX6XEqf36Xd4iAI6u7hO3rYHMn5NNiW5jYiDhn/Jw9OPDOIGotY87AKuU5IbLMmj2kX9jQYyDAWj7lR9SiqYfa1IJAUbGH7CQylo3ED6prTz/WhkWaskz7bzBV8F0k4N/4IHgZppIFXYMt8KdDqdTvdrXzl2ZVzdqjbIAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7600-3329","institution":"Nantes University Hospital, CRCINA, INSERM, CNRS, Angers University, Nantes University","correspondingAuthor":true,"prefix":"","firstName":"Benoit","middleName":"","lastName":"Tessoulin","suffix":""},{"id":469795044,"identity":"da35b9b0-9dee-49d6-af2b-4e1916927f14","order_by":1,"name":"Valentin LETAILLEUR","email":"","orcid":"https://orcid.org/0000-0003-0522-6976","institution":"CHU Nantes","correspondingAuthor":false,"prefix":"","firstName":"Valentin","middleName":"","lastName":"LETAILLEUR","suffix":""},{"id":469795045,"identity":"d09c32dc-8938-494f-bc40-1317aa1ed94c","order_by":2,"name":"Isabelle Chaillol","email":"","orcid":"","institution":"LYSARC","correspondingAuthor":false,"prefix":"","firstName":"Isabelle","middleName":"","lastName":"Chaillol","suffix":""},{"id":469795046,"identity":"ab5fcfac-1696-4845-91e7-b00c188d786a","order_by":3,"name":"Fanny Cherblanc","email":"","orcid":"","institution":"LYSARC","correspondingAuthor":false,"prefix":"","firstName":"Fanny","middleName":"","lastName":"Cherblanc","suffix":""},{"id":469795047,"identity":"6a6f4906-d606-4166-9291-82531827b1f9","order_by":4,"name":"Hervé Ghesquières","email":"","orcid":"","institution":"Centre Hospitalier Lyon Sud","correspondingAuthor":false,"prefix":"","firstName":"Hervé","middleName":"","lastName":"Ghesquières","suffix":""},{"id":469795048,"identity":"12b0bda3-1281-42ed-b924-f8577b124b71","order_by":5,"name":"Frédéric Peyrade","email":"","orcid":"","institution":"Antoine Lacassagne Center","correspondingAuthor":false,"prefix":"","firstName":"Frédéric","middleName":"","lastName":"Peyrade","suffix":""},{"id":469795049,"identity":"75681958-6219-411e-a135-c3203bdf5775","order_by":6,"name":"Stéphanie Guidez","email":"","orcid":"","institution":"CHU de Poitiers","correspondingAuthor":false,"prefix":"","firstName":"Stéphanie","middleName":"","lastName":"Guidez","suffix":""},{"id":469795050,"identity":"dd900823-125c-45ce-a4e3-ca9ad1a73f77","order_by":7,"name":"Fontanet Bijou","email":"","orcid":"","institution":"Institut Bergonié","correspondingAuthor":false,"prefix":"","firstName":"Fontanet","middleName":"","lastName":"Bijou","suffix":""},{"id":469795051,"identity":"f81b8794-e64a-4e3d-b8db-2584ff1ee124","order_by":8,"name":"Pierre Sesques","email":"","orcid":"https://orcid.org/0000-0001-8264-822X","institution":"Hospices Civils de Lyon","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Sesques","suffix":""},{"id":469795052,"identity":"7568be26-e3e4-4d15-a406-81f4f4547a30","order_by":9,"name":"Cédric Rossi","email":"","orcid":"","institution":"University Hospital F. Mitterrand and Inserm UMR 1231, Dijon","correspondingAuthor":false,"prefix":"","firstName":"Cédric","middleName":"","lastName":"Rossi","suffix":""},{"id":469795053,"identity":"4bc4b87a-555f-4550-9ffb-398477bd1199","order_by":10,"name":"Luc-Matthieu Fornecker","email":"","orcid":"","institution":"Hôpitaux Universitaires de Strasbourg","correspondingAuthor":false,"prefix":"","firstName":"Luc-Matthieu","middleName":"","lastName":"Fornecker","suffix":""},{"id":469795054,"identity":"5b15bbf9-e132-4e1a-8ba5-45e2a6889a12","order_by":11,"name":"Ludovic Fouillet","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Saint-Etienne","correspondingAuthor":false,"prefix":"","firstName":"Ludovic","middleName":"","lastName":"Fouillet","suffix":""},{"id":469795055,"identity":"668ad630-c9a2-4d0b-8c53-d23a3b423171","order_by":12,"name":"Sylvain Carras","email":"","orcid":"https://orcid.org/0000-0002-0796-7914","institution":"Univ. Grenoble-Alpes, CHU Grenoble Alpes","correspondingAuthor":false,"prefix":"","firstName":"Sylvain","middleName":"","lastName":"Carras","suffix":""},{"id":469795056,"identity":"e6fccd68-ec98-4f68-9f0f-4139d966ceed","order_by":13,"name":"Loic Chartier","email":"","orcid":"","institution":"The Lymphoma Academic Research Organisation , Statistic, Centre Hospitalier Lyon Sud,","correspondingAuthor":false,"prefix":"","firstName":"Loic","middleName":"","lastName":"Chartier","suffix":""},{"id":469795057,"identity":"23bbd2ed-958e-4fa5-b5c0-0f049f5f6215","order_by":14,"name":"Aurelien Belot","email":"","orcid":"","institution":"LYSARC","correspondingAuthor":false,"prefix":"","firstName":"Aurelien","middleName":"","lastName":"Belot","suffix":""},{"id":469795058,"identity":"0ae59a1f-0abf-4a35-ac8c-679000554f11","order_by":15,"name":"Lucie Oberic","email":"","orcid":"","institution":"Department of hematology, IUC Toulouse Oncopole","correspondingAuthor":false,"prefix":"","firstName":"Lucie","middleName":"","lastName":"Oberic","suffix":""},{"id":469795059,"identity":"a97c84ba-2848-49bc-b250-0cf434b4cf98","order_by":16,"name":"Fabrice Jardin","email":"","orcid":"https://orcid.org/0000-0002-6804-7943","institution":"Centre Henri Bequerel, FR","correspondingAuthor":false,"prefix":"","firstName":"Fabrice","middleName":"","lastName":"Jardin","suffix":""}],"badges":[],"createdAt":"2025-06-04 17:35:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6822758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6822758/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41408-025-01374-x","type":"published","date":"2025-11-03T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84721358,"identity":"6dfc29c3-0f2e-4486-82d9-bd6466ae2e0b","added_by":"auto","created_at":"2025-06-16 15:07:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":379525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eStudy flow-chart\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003etFL = transformed follicular lymphoma. PS = propensity score. mAb = monoclonal antibody. * = Effective sample size of the pseudo-population after weighting, which is close to real samples.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/3e31f7c5c7b1bd1780c23fe5.png"},{"id":84723420,"identity":"a61e149a-13c3-4a89-9e5d-9bb54ce6258a","added_by":"auto","created_at":"2025-06-16 15:23:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":265798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eBalance of covariates included in the propensity score between (A) SENIOR standard arm or (B) SENIOR experimental arm and Mixed SCAs before and after sIPTW weighting\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/ce094a9bbedac9739c9523d9.png"},{"id":84722562,"identity":"0570e037-d1d3-4cfd-8e30-337f37f7b6c8","added_by":"auto","created_at":"2025-06-16 15:15:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":651966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eA-B: OS comparison between Mixed SCA 1 and SENIOR control arm before (A) and after (B) weighting. C-D: OS comparison between Mixed SCA 2 and SENIOR experimental arm (C) before and (D) after weighting\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/febe6a4ff037b8517548ed44.png"},{"id":84721367,"identity":"611d127b-0fe6-4d8e-9209-bb7730ac78ae","added_by":"auto","created_at":"2025-06-16 15:07:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":267779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eBalance of covariates included in the propensity score between (A) SENIOR standard arm or (B) SENIOR experimental arm and REALYSA-SCAs before and after sIPTW weigting\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/dfdea01bd6628e28d152bee0.png"},{"id":84722565,"identity":"9c58f744-1a36-42db-8e9e-4adcb6cb790e","added_by":"auto","created_at":"2025-06-16 15:15:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":627157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eA-B: OS comparison between REALYSA-SCA 1 and SENIOR control arm before (A) and after (B) weighting. C-D: OS comparison between REALYSA-SCA 2 and SENIOR experimental arm (C) before and (D) after weighting.\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/4324b9b08e887e77a8a6dda6.png"},{"id":95090363,"identity":"ad40585a-5e60-4a0a-af1d-b9b2dc8202c0","added_by":"auto","created_at":"2025-11-04 08:09:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3477751,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/cd87bfc5-56a6-477e-9a0d-df262347ef5f.pdf"},{"id":84721356,"identity":"c07ff209-0931-4b8e-b000-4572a3c2a0ec","added_by":"auto","created_at":"2025-06-16 15:07:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eTable I: Patients’ characteristics from the confirmed diagnosis set\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eCharacteristics of all patients included, from their cohort of origin are shown, before propensity score assessment and weighting procedures. Values are displayed as n(%), or mean(SD) when specified. SD: standard deviation; IPI: international prognostic index; LDH: lactate dehydrogenase; HGBL: high grade B-cell lymphoma; DLBCL: diffuse large B-cell lymphoma.\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/d9bd874e93b29c804b2bb44f.xlsx"},{"id":84722558,"identity":"940b9237-bf0a-40a0-8562-97639168ace4","added_by":"auto","created_at":"2025-06-16 15:15:55","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eTable II: Patients’ characteristics from SENIOR standard or SENIOR experimental arm and Mixed SCA 1 or 2 from the PS set before and after sIPTW weighting\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/0cec1bbffae1fd8f7a89090a.xlsx"},{"id":84721359,"identity":"2830a6cd-4f6c-43f3-893c-8336e4a84583","added_by":"auto","created_at":"2025-06-16 15:07:56","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eTable III: Patients’ characteristics from SENIOR standard or SENIOR experimental arm and REALYSA-SCA from the PS set, before and after sIPTW weighting\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/08d182a9826828fc1cb03e3a.xlsx"},{"id":84723913,"identity":"86755d50-9db1-4401-a1dd-56b1293c432f","added_by":"auto","created_at":"2025-06-16 15:31:56","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eSupplemental Table I: HRs with 95% CI of OS comparison between SENIOR standard or experimental arm and Mixed-SCAs or REALYSA-SCAs, depending on missing data management method (across, complete case or within) used.\u003c/u\u003e\u003c/p\u003e","description":"","filename":"SupplementalTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/5c36ab4bbb9c366f32f7dfe8.xlsx"},{"id":84721370,"identity":"7230e262-7fde-428a-94b9-737683eda783","added_by":"auto","created_at":"2025-06-16 15:07:56","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":160078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eSupplemental Figure 1: Balance between Mixed SCAs and (A) SENIOR control arm or (B) SENIOR experimental arm: SMD for each covariate after sIPTW using \"within\" missing management method according to different imputations\u003c/u\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/d0edb833aa84de5695207c57.tif"},{"id":84721378,"identity":"87f1e9a1-ed8f-44e3-9e1e-3fbc0b3bda5a","added_by":"auto","created_at":"2025-06-16 15:07:56","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":172462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eSupplemental Figure 2: Balance between REALYSA-SCAs and (A) SENIOR control arm or (B) SENIOR experimental arm: SMD for each covariate after sIPTW using \"within\" missing management method according to different imputations\u003c/u\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6822758/v1/04efd4e8f5d0379c91b33217.tif"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Synthetic control arm from mixed clinical trials and real-world data from LYSA group for untreated diffuse large B cell lymphoma patients aged over 80 years: a bona fide strategy for innovative clinical trials","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiffuse large B cell lymphoma (DLBCL) is the most common lymphoid malignancy among elderly, and about 25% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) of new diagnoses concern patients aged 80 years or older (\u0026ge;\u0026thinsp;80y.o). Besides obvious differences related to treatment tolerance and management compared to the younger population, raising knowledge, in molecular cell of origin subclassification, for example, demonstrates that DLBCL of the elderly may have specific aspects (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Yet, they are underrepresented in clinical trials, and few trials focus on very elderly patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Recommended treatment in first line setting, as established by two phase II clinical trials, LNH03-7B (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and LNH09-7B (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), is Rituximab and reduced CHOP (Cyclophosphamide, Doxorubicine, Vincristine, Prednisone) combination (R-miniCHOP), with a 2-year overall survival (OS) approaching 60%. Alternatively, regimens replacing or omitting anthracyclines can be proposed for frail patients or those with cardiac function impairment. (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Pre-phase treatment, combining oral prednisone with or without vincristine and cyclophosphamide, is recommended for the elderly and should be considered after 80 years-old, as it has been shown to be beneficial for \u003cem\u003eperformans status\u003c/em\u003e improvement, allowing better tolerance during subsequent R-miniCHOP treatment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore than a decade after LNH09-7B, improving 1st line treatment outcomes remains tricky in this population, despite recent overall advances brought by targeted therapies and immunomodulatory agents. Indeed, although the lenalidomide and R-miniCHOP combination (R\u0026sup2;miniCHOP) had an appealing rational, as the activated B-cell (ABC) DLBCL subtype increases with aging (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), SENIOR (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), the first -and only published to date- randomized phase III clinical trial (RCT) focused on \u0026ge;\u0026thinsp;80 y.o patients, failed to show an improvement in OS, the primary endpoint, which can be partly explained by toxicity issues. Recruitment of the \u0026ge;\u0026thinsp;80 y.o. in clinical trials can be more challenging than for the younger patients, explaining partly the lack of focused RCT. New combinations are in evaluation in ongoing trials (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), including RCTs (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), that will hopefully bring new treatment options with hypothetical approvals.\u003c/p\u003e \u003cp\u003eAt a lower level of proof, phase II clinical trials can bring new opportunities in 1st line management. However, comparing results from different phase II trials is not recommended as variations in patient populations, study design or endpoints can lead to misleading conclusions. Use of synthetic control arms (SCA) in clinical trials with innovative designs could improve statistical confidence, replacing either entirely the internal control arm (analysis of a phase II CT), or partially (phase III RCT with a mixed control arm composed of randomized patients and historical patients) (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Even if DLBCL\u0026thinsp;\u0026ge;\u0026thinsp;80 y.o cannot be considered as a rare disease, the lack of representation and recruitment issues in clinical trials can justify the use of SCA, with rigorous statistical methods to control covariates balance between arms and to reduce biases related to the lack of randomization (\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and general rules, as defined by agencies, have been published (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we aimed to build a SCA from mixed real-world and clinical trial data for \u0026ge;\u0026thinsp;80 y.o DLBCL patients at first line of treatment, and to validate its clinical and statistical relevance by applying it to the SENIOR trial, the only published RCT in this population.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and study design\u003c/h2\u003e \u003cp\u003ePatient-level data from REALYSA (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and LNH09-7B (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) databases were retrieved for SCA patients, and data from SENIOR trial (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) were used for validation. REALYSA is a French real-world multicentric observational cohort recruiting newly diagnosed lymphoma patients since November 2018. LNH09-7B was a phase II clinical trial assessing efficacy of Ofatumumab and miniCHOP preceded by a pre-phase treatment (vincristine and oral prednisone) as first line treatment of DLBCL patients\u0026thinsp;\u0026ge;\u0026thinsp;80 y.o, which resulted in a 2-years OS of 64.7% [95% CI: 55.3\u0026ndash;72.7]. SENIOR trial was a phase III RCT assessing RminiCHOP \u003cem\u003evs.\u003c/em\u003e R-Lenalidomide-miniCHOP, with a pre-phase treatment, as first line treatment of DLBCL patients\u0026thinsp;\u0026ge;\u0026thinsp;80 y.o, with similar inclusion criteria as LNH09-7B. This study failed to show an improvement in OS with the addition of lenalidomide (2-years OS of 66% for RminiCHOP versus 65.7% for R\u0026sup2;miniCHOP arm). Patients from LNH09-7B had their treatment started from June 2010 to November 2011, and those from SENIOR from October 2014 to September 2017.\u003c/p\u003e \u003cp\u003eFrom REALYSA cohort, we included patients aged\u0026thinsp;\u0026ge;\u0026thinsp;80 y.o., treated with RminiCHOP combination as first line treatment, included in the cohort from December 2018 to 31 December 2021. Patients with \u003cem\u003eperformans status\u003c/em\u003e (ECOG) of 3 or 4 and Ann Arbor stage I were not included to match SENIOR inclusion criteria. Data were exported from the registry the 25th of June 2024.\u003c/p\u003e \u003cp\u003eClinical trials patients with non-compliant diagnoses (other than DLBCL or high-grade B cell lymphoma (HGBL)) according to anatomopathological centralized reviews realized in each trial or local diagnosis for REALYSA were excluded.\u003c/p\u003e \u003cp\u003eThe study was designed as follows: first step was to build a mixed SCA (\u0026ldquo;Mixed SCA 1\u0026rdquo;) from real-world data (REALYSA) and clinical trial data (LNH09-7B) adjusted on SENIOR control arm and to evaluate if it could mimic the internal control arm from SENIOR. Second step was to build a mixed SCA (\u0026ldquo;Mixed SCA 2\u0026rdquo;) adjusted on experimental arm of SENIOR, to evaluate if we could replicate the efficacy results from SENIOR study by switching the internal control arm by the Mixed SCA 2. For each step, populations were balanced as described below.\u003c/p\u003e \u003cp\u003ePrimary and only endpoint for arms comparison after weighting was OS, measured from date of inclusion (or date of diagnosis in REALYSA) to date of death. As a high number of REALYSA patients had a limited follow-up period, we chose to censor all patients at 24 months for all cohorts.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMatching procedures:\u003c/h3\u003e\n\u003cp\u003ePropensity scores (PS) were estimated for each patient included using logistic regression with following covariates : sex, age (spline), Ann Arbor stage (I-II versus III-IV), Performans Status (ECOG) (0 vs. 1 vs. 2), number of extranodal sites involved (\u0026lt;\u0026thinsp;2 versus \u0026ge;\u0026thinsp;2), international prognostic index (IPI) score (0\u0026ndash;2 versus 3\u0026ndash;5), B symptoms, lactate dehydrogenase (LDH) level (normal versus over the upper limit of normal (ULN)), bulky mass\u0026thinsp;\u0026gt;\u0026thinsp;10cm, albumin level in gram per litter (g/L) (spline).\u003c/p\u003e \u003cp\u003eFor comparison between the Mixed SCA 1 and SENIOR internal control arm, PS allows to estimate the probability for a patient to receive \u0026ldquo;RminiCHOP via SENIOR\u0026rdquo;. For comparison between the Mixed SCA 2 versus SENIOR experimental arm, PS allows to estimate the probability for a patient to receive R\u0026sup2;miniCHOP.\u003c/p\u003e \u003cp\u003eThen, patients\u0026rsquo; covariates between arms were weighted using the stabilized inverse probability of treatment weighting (sIPTW) statistical approach, the probability of treatment being reflected by PS (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo avoid positivity violations, patients with extreme PS (below 0.1 or over 0.9) were excluded (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). After matching procedures, standardized mean differences (SMD) were estimated between arms for each covariate included for PS estimation to check balance in covariates\u0026rsquo; distributions. With the statistical approach used, sIPTW allowed to estimate the average treatment effect (ATE).\u003c/p\u003e\n\u003ch3\u003eMissing data management:\u003c/h3\u003e\n\u003cp\u003eDue to the high number of missing data for patients from REALYSA for some covariates, multiple imputation method \u0026ldquo;across\u0026rdquo; was performed (15 imputations by patient) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). That generated 15 complete datasets. PS were calculated for each of the datasets. Then, for each patient, the median of the 15 PS was used for weighting. The SMD presented in this manuscript were calculated on real data, \u003cem\u003ei.e.\u003c/em\u003e before imputation. With the \u0026ldquo;across\u0026rdquo; missing management method, outcome analyses are performed on one dataset.\u003c/p\u003e\n\u003ch3\u003eStatistics for outcome analyses:\u003c/h3\u003e\n\u003cp\u003eAs first objective was to assess if the Mixed SCA 1 could mimic the internal control arm of SENIOR, Hazard Ratios (HR) 95% confidence intervals were expected to include 1. The second objective was to reproduce SENIOR results by replacing the internal control arm by the Mixed SCA 2, HR were expected to overlap with the results obtained in SENIOR trial (HR [95%CI] of 0.996 [0.66\u0026ndash;1.51]), including 1 in the HR 95%CI.\u003c/p\u003e \u003cp\u003eOS curves were generated using Kaplan-Meier method, and OS curves were compared with log-rank test. HR and 95% confidence intervals (CI) were calculated using Cox proportional hazards model. One-sided p-value below 0.05 was considered significant. Analyses were performed using SAS software 9.4.\u003c/p\u003e\n\u003ch3\u003eSensitivity analyses:\u003c/h3\u003e\n\u003cp\u003eSensitivity analyses were performed using data coming only from REALYSA cohort to evaluate the possibility to build a well-balanced SCA using only real-world data (REALYSA-SCA), with the same methodology used for the mixed SCA building.\u003c/p\u003e \u003cp\u003eAdditionally, we performed sensitivity analysis using different missing data management methods on Mixed SCAs analyses or REALYSA-SCA analyses. \u0026ldquo;Complete cases\u0026rdquo; method excludes patients with missing data. For \u0026ldquo;within\u0026rdquo; method, analyses are performed on each imputed dataset (15 analyses) and results were combined using Rubin\u0026rsquo;s rules (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; characteristics:\u003c/h2\u003e \u003cp\u003eOverall, in this study, we included 73 patients from LNH09-7B and 97 patients from REALYSA to source mixed SCAs, and 98 or 104 patients from SENIOR standard or experimental arm, respectively, to assess Mixed SCAs relevance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This population will be referred as confirmed Diagnosis Set. All included patients received anti-CD20 monoclonal antibody (mAb)\u0026thinsp;+\u0026thinsp;miniCHOP based regimen as first line treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable I\u003c/b\u003e shows characteristics of patients from each cohort included in confirmed Diagnosis Set. Most patients included in this study had an intermediary-high or high risk DLBCL (77.1% of overall patients with IPI score at 3 or higher).\u003c/p\u003e \u003cp\u003ePS were calculated for all patients before each sIPTW weighting procedure. Five patients were excluded (Based on Mixed-SCA 1 PS: 2 from SENIOR standard arm, and based on Mixed-SCA 2: 1 from SENIOR experimental arm and 2 from Mixed-SCA 2) because of extreme values (above 0.9 or below 0.1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Final population was included in the \u0026ldquo;PS set\u0026rdquo;. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn the PS set, some covariates were unbalanced before weighting, with an absolute SMD above 0.1 for 4 covariates (Sex, Performans Status 0 and 2, Mass\u0026thinsp;\u0026gt;\u0026thinsp;10cm and Ann Arbor stage) before sIPTW between SENIOR Standard arm and Mixed SCA 1 and for 5 covariates (Sex, Mass\u0026thinsp;\u0026gt;\u0026thinsp;10cm, LDH, IPI, B symptoms) between SENIOR experimental arm and Mixed SCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). SMD were also calculated on a pool of the 15 datasets with imputation and were similar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWeighting procedures:\u003c/h3\u003e\n\u003cp\u003eFinal populations in the PS set were weighted with the sIPTW method: SENIOR Standard arm with Mixed SCA 1 and SENIOR Experimental arm with Mixed SCA 2. \u003cb\u003eTable II\u003c/b\u003e shows patients\u0026rsquo; characteristics distribution in the 4 arms before and after weighting.\u003c/p\u003e \u003cp\u003eUsing the \u0026ldquo;across\u0026rdquo; method to manage missing data, weighting procedures with sIPTW were efficient to balance covariates between arms as all SMD for covariates included in the PS were below 0.10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo explore if the missing data management method used in this study could impact weighting efficiency, we also analyzed balance of covariates using the \u0026ldquo;complete case\u0026rdquo; or \u0026ldquo;within\u0026rdquo; method. SMD for all covariates were \u0026lt;\u0026thinsp;0.1 whatever the method used for missing data management. Moreover, in the \u0026ldquo;within\u0026rdquo; method, at different imputations (from 1 to 15), SMD for all covariates were \u0026lt;\u0026thinsp;0.1 and variation of SMD values across the different imputations was very low (\u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOutcome analysis:\u003c/h2\u003e \u003cp\u003eFirst step was to assess if Mixed SCA 1 could reproduce SENIOR control arm\u0026rsquo;s OS. OS was not significantly different between SENIOR control arm and Mixed SCA 1, with an HR [95%CI] of 0.86 [0.57\u0026ndash;1.32] (p\u0026thinsp;=\u0026thinsp;0.493) before weighting, and an HR of 0.79 [0.52\u0026ndash;1.20] (p\u0026thinsp;=\u0026thinsp;0.277) after weighting with sIPTW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, A B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSecond step was to assess the reproducibility of SENIOR trial results by switching SENIOR control arm by Mixed SCA 2. In our study, OS was not significantly different between SENIOR experimental arm and Mixed SCA 2 with an HR of 0.83 [0.55\u0026ndash;1.25] (p\u0026thinsp;=\u0026thinsp;0.3631) before weighting, and an HR of 0.74 [0.49\u0026ndash;1.12] (p\u0026thinsp;=\u0026thinsp;0.165) after weighting with sIPTW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, C \u003cb\u003eD\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThere was no statistically significant difference in OS between SENIOR arms and Mixed SCAs using different missing data management methods (\u003cb\u003eSupplemental Table I\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eREALYSA-SCA:\u003c/h2\u003e \u003cp\u003eWe also assessed feasibility and viability of SCAs (REALYSA-SCAs) built with real-world data from REALYSA cohort only, with the same methods as the Mixed SCAs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTables III\u003c/b\u003e shows patients\u0026rsquo; characteristics from SENIOR control arm or experimental arm compared to patients from REALYSA-SCAs before and after sIPTW. More covariates were imbalanced before weighting compared to analyses conducted with Mixed SCAs, but after weighting, SMDs of all covariates were \u0026lt;\u0026thinsp;0.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Weighting procedures were efficient, irrespective of the weighting method or the missing data management method used (\u003cb\u003eSupplemental Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOS was not significantly different between SENIOR control arm and REALYSA-SCA-1 with an HR [95%CI] of 0.89 [0.56\u0026ndash;1.44] (p\u0026thinsp;=\u0026thinsp;0.640) before weighting and an HR of 0.90 [0.56\u0026ndash;1.43] (p\u0026thinsp;=\u0026thinsp;0.478) after sIPTW. Similarly, OS was not significantly different between SENIOR experimental arm and REALYSA-SCA-2 with an HR of 0.84 [0.53\u0026ndash;1.35] (p\u0026thinsp;=\u0026thinsp;0.474) before weighting and an HR of 0.88 [0.55\u0026ndash;1.41] (p\u0026thinsp;=\u0026thinsp;0.612) after weighting with sIPTW (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e,A-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOS comparison results between REALYSA-SCAs and SENIOR standard or experimental arm obtained in sensitivity analyses were close to those obtained with the main methodology, and no statistically significant difference was observed (\u003cb\u003eSupplemental Table I\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we showed that a well-balanced synthetic control arm (SCA) designed for \u0026ge;\u0026thinsp;80y.o DLBCL patients in the first line setting, with data extracted from historical clinical trial and real-world settings, could mimic the internal control arm of a RCT. Switching the internal control arm by a SCA reproduced efficacy results based on OS comparison, with an HR [95%IC] of 0.743 [0.494\u0026ndash;1.118] compared to an HR of 0.996 [0.66\u0026ndash;1.51] in SENIOR trial. This research demonstrates that we should open the field of newly-designed trials to improve our clinical-trial accrual and overall clinical benefit, even in the very elderly setting.\u003c/p\u003e \u003cp\u003eOur selected balancing procedure with sIPTW method was highly efficient with all SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1 for covariates included in the Propensity Score calculation along with a low drop-rate of \u0026ldquo;extreme patients\u0026rsquo; (0,7 to 1,4% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)), ensuring robustness of the results. We used mixed sources of real-world data and clinical trial data to build this mixed SCA, to strengthen the extent of reliability of our results in a perspective of \"real-life\" incoming phase III clinical trials. Surprisingly, we observed a limited number of real-world-sourced patients who would be excluded after applying inclusion criteria (\u003cem\u003ePerformans status\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;3 and Ann Arbor stage\u0026thinsp;\u0026gt;\u0026thinsp;I). Regarding patients\u0026rsquo; characteristics, those results showed that \u0026ge;\u0026thinsp;80y.o patients with DLBCL in first line setting are quite similar despite very different inclusion periods between LNH09-7B, SENIOR and REALYSA. Indeed, most patients\u0026rsquo; characteristics were already balanced before matching as only few covariates had a high SMD despite the absence of randomization and the use of real-world data. It suggests that 1) by applying SENIOR trial population inclusion criteria we selected a substantially homogeneous population of DLBCL elderly patients to build SCAs and 2) SENIOR trial enrolled an unbiased DLBCL elderly population with immuno-chemotherapy intent of treatment. Of note, analysis based on a SCA only built with these real-world data showed that patients could also be well balanced with SENIOR patients from both internal control arm or experimental arm with the sIPTW method (REALYSA-SCA) with an HR [95%CI] of 0.895 [0.560\u0026ndash;1.429] and 0.877 [0.547\u0026ndash;1.407] respectively. Some limitations can be taken into account for our study. Period of data accrual is different between LNH09-7B trial data (2010 to 2011), and REALYSA (2018 to 2021), and we cannot rule out that supportive care may have improved during this 10y time-frame. Furthermore, we couldn\u0026rsquo;t incorporate prognostic geriatric assessments (\u003cem\u003ee.g.\u003c/em\u003e Instrumental Activities of Daily Living (IADL) scale) into the PS calculation, because of the lack of this information in all datasets. In our dataset, data were prospectively collected, with a low rate of missing data except for two covariates (mass\u0026thinsp;\u0026gt;\u0026thinsp;10cm and albumin level with \u0026gt;\u0026thinsp;10% of missing data from REALYSA patients) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Moreover, we showed that missing data could be reliably imputed, allowing us to keep steady sample size with robust outcome analyses results. Indeed, sensitivity analyses for missing data management with \u0026ldquo;complete cases\u0026rdquo;, or \u0026ldquo;within\u0026rdquo; multiple imputation methods led to same conclusions as for the main (\u0026ldquo;across\u0026rdquo;) method.\u003c/p\u003e \u003cp\u003eFrom a statistical point-of-view, we chose sIPTW weighting method because it resulted in a pseudo-population with stable and comparable sample sizes, as opposed to IPTW which would have here resulted in a larger pseudo-population (that may result in an increased rate of type 1 error). As illustrated in sensitivity analyses, CI were narrower with IPTW compared to sIPTW matching, but we still obtained consistent results with IPTW.\u003c/p\u003e \u003cp\u003eOther examples of SCA building and validation emerged recently in hematology field using data at an individual level from historical clinical trials or real-world data with PS-based methods to control for confounding variables (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). For example, another study also used REALYSA data to build a SCA with newly diagnosed patients with advanced Hodgkin lymphoma and reproduced efficacy results of the phase III RCT AHL2011 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe believe our study demonstrates that in a difficult to study population, SCA could be used as a \u003cem\u003ebona-\u003c/em\u003efide strategy to improve very elderly DLBCL patient\u0026rsquo;s outcomes by allowing trials with \u0026ldquo;\u003cem\u003ein silico\u0026rdquo;\u003c/em\u003e control arms along with more promising experimental arms. Indeed, SENIOR is currently the only published RCT in this setting and recruited 249 patients in three years within 100 centers in France and Belgium, illustrating the challenges to enroll these patients.\u003c/p\u003e \u003cp\u003eThe mixed SCAs we built could be used to help assessment of new combinations with targeted therapies that could improve OS compared to RminiCHOP based regimen, for which the adjunction of another drug seems tricky due to toxicity issues in this population. In our opinion, OS is the most relevant primary outcome to compare SCA to another arm, especially in the elderly population, as first progression is soon followed by death in this low-reserve population. Furthermore, health authorities are now giving weight to these indirect statistically sound strategies to allow market access to new treatments (if the control arm is the right one), and we could increase this weight by new design of trials. For instance, comparative trials with control arms composed of 50% of external patients and 50% of randomized patients (in order to keep toxicity comparisons available) could accelerate accrual time by reducing the number of patients to include, or, with a steady number of patients to include, allow more patients to be included in the experimental arm (2:1 randomization). To conclude, we built here a SCA from mixed clinical trial and real-world data for \u0026ge;\u0026thinsp;80 y.o DLBCL patients in first line setting that could be used to build comparative trials with an innovative design, that could help to explore the ability of new treatment combinations to improve survival in this otherwise poorly-represented population in clinical trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eVL, BT, IC \u0026amp; FC designed, coordinated the research, analyzed, interpreted the data, and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors thank all the patients and their families for their confidence, as well as the LYSA investigators and the CRA teams, LYSARC staff, the Hospices Civils de Lyon (sponsor of REALYSA study). The initial studies (SENIOR, LNH09-7B and REALYSA) were financially supported by Roche, Takeda, Janssen, Amgen, Celgene-Bristol Myers Squibb, Astra-Zeneca, Abbvie and GSK.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKanapuru B, Singh H, Kwitkowski V, Blumenthal G, Farrell AT, Pazdur R. Older adults in hematologic malignancy trials: Representation, barriers to participation and strategies for addressing underrepresentation. Blood Reviews. 2020;43:100670.\u003c/li\u003e\n\u003cli\u003eMareschal S, Lanic H, Ruminy P, Bastard C, Tilly H, Jardin F. The proportion of activated B-cell like subtype among de novo diffuse large B-cell lymphoma increases with age. Haematologica. 2011;96(12):1888\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eWilson WH, Wright G, Huang DW, Hodkinson B, Balasubramanian S, Fan Y, et al. Effect of Ibrutinib with R-CHOP Chemotherapy in Genetic Subtypes of DLBCL. Cancer Cell. 2021;39(12):1643\u0026ndash;1653.e3.\u003c/li\u003e\n\u003cli\u003ePeyrade F, Jardin F, Thieblemont C, Thyss A, Emile JF, Castaigne S, et al. Attenuated immunochemotherapy regimen (R-miniCHOP) in elderly patients older than 80 years with diffuse large B-cell lymphoma: a multicentre, single-arm, phase 2 trial. The Lancet Oncology. 2011;12(5):460\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003ePeyrade F, Bologna S, Delwail V, Emile JF, Pascal L, Ferm\u0026eacute; C, et al. Combination of ofatumumab and reduced-dose CHOP for diffuse large B-cell lymphomas in patients aged 80 years or older: an open-label, multicentre, single-arm, phase 2 trial from the LYSA group. The Lancet Haematology. 2017;4(1):e46\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eMoccia AA, Schaff K, Freeman C, Hoskins PJ, Klasa RJ, Savage KJ, et al. Long-term outcomes of R-CEOP show curative potential in patients with DLBCL and a contraindication to anthracyclines. Blood Advances. 2021;5(5):1483\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eFields PA, Townsend W, Webb A, Counsell N, Pocock C, Smith P, et al. De Novo Treatment of Diffuse Large B-Cell Lymphoma With Rituximab, Cyclophosphamide, Vincristine, Gemcitabine, and Prednisolone in Patients With Cardiac Comorbidity: A United Kingdom National Cancer Research Institute Trial. JCO. 2014;32(4):282\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLaribi K, Denizon N, Bolle D, Truong C, Besan\u0026ccedil;on A, Sandrini J, et al. R-CVP regimen is active in frail elderly patients aged 80 or over with diffuse large B cell lymphoma. Ann Hematol. 2016;95(10):1705\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003ePfreundschuh M, Schubert J, Ziepert M, Schmits R, Mohren M, Lengfelder E, et al. Six versus eight cycles of bi-weekly CHOP-14 with or without rituximab in elderly patients with aggressive CD20\u0026thinsp;+\u0026thinsp;B-cell lymphomas: a randomised controlled trial (RICOVER-60). The Lancet Oncology. 2008;9(2):105\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eLugtenburg PJ, Mutsaers PGNJ. How I treat Elderly Patients with DLBCL in the frontline setting. Blood. 2022;blood.2020008239.\u003c/li\u003e\n\u003cli\u003eOberic L, Peyrade F, Puyade M, Bonnet C, Dartigues-Cuill\u0026egrave;res P, Fabiani B, et al. Subcutaneous Rituximab-MiniCHOP Compared With Subcutaneous Rituximab-MiniCHOP Plus Lenalidomide in Diffuse Large B-Cell Lymphoma for Patients Age 80 Years or Older. JCO. 2021;39(11):1203\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eTessoulin B, Depaus J, Feugier P, Fouillet L, Abraham J, Amorin S, et al. Verlen, \u0026ldquo;Very Elderly Rituximab Associated to Lenalidomide - Tafasitamab Combination in Frontline DLBCL Patients\u0026rdquo;, a Phase II Open-Label Study Evaluating Efficacy of Lenalinomide and Tafasitamab Combination Associated to Rituximab in Frontline Diffuse Large B-Cell Lymphoma Patients of 80 y/o or Older from the Lysa Group. Blood. 2023;142(Supplement 1):3094.\u003c/li\u003e\n\u003cli\u003eJerkeman M, Lepp\u0026auml; S, Hamfjord J, Brown P, Ekberg S, Jos\u0026eacute; Mar\u0026iacute;a Ferreri A. S227: INITIAL SAFETY DATA FROM THE PHASE 3 POLAR BEAR TRIAL IN ELDERLY OR FRAIL PATIENTS WITH DIFFUSE LARGE CELL LYMPHOMA, COMPARING R-POLA-MINI-CHP AND R-MINI-CHOP. Hemasphere. 2023;7(Suppl):e91359ec.\u003c/li\u003e\n\u003cli\u003eBrem EA, Li H, Beaven AW, Caimi PF, Cerchietti L, Alizadeh A, et al. SWOG 1918: A Phase II/III randomized study of R-miniCHOP with or without oral azacitidine (CC-486) in participants age 75 years or older with newly diagnosed aggressive non-Hodgkin lymphomas - Aiming to improve therapy, outcomes, and validate a prospective frailty tool. J Geriatr Oncol. 2022;13(2):258\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eThorlund K, Dron L, Park JJH, Mills EJ. Synthetic and External Controls in Clinical Trials \u0026ndash; A Primer for Researchers. Clin Epidemiol. 2020;12:457\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eLambert J, Lenglin\u0026eacute; E, Porcher R, Thi\u0026eacute;baut R, Zohar S, Chevret S. Enriching single-arm clinical trials with external controls: possibilities and pitfalls. Blood Adv. 2022;7(19):5680\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eCucherat M, Laporte S, Delaitre O, Behier JM, d\u0026rsquo;Andon A, Binlich F, et al. From single-arm studies to externally controlled studies. Methodological considerations and guidelines. Therapies. 2020;75(1):21\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eAustin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083\u0026ndash;107.\u003c/li\u003e\n\u003cli\u003eAustin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med. 2014;33(7):1242\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eAllan V, Ramagopalan SV, Mardekian J, Jenkins A, Li X, Pan X, et al. Propensity score matching and inverse probability of treatment weighting to address confounding by indication in comparative effectiveness research of oral anticoagulants. J Comp Eff Res. 2020;9(9):603\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eSchulte PJ, Mascha EJ. Propensity Score Methods: Theory and Practice for Anesthesia Research. Anesthesia \u0026amp; Analgesia. 2018;127(4):1074.\u003c/li\u003e\n\u003cli\u003eXie J, Liu C. Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data. Stat Med. 2005;24(20):3089\u0026ndash;110.\u003c/li\u003e\n\u003cli\u003eMotrinchuk AS, Belousov DYu. Considerations for the design and conduct of externally controlled trials for drug and biological products. MyRWD. 2023;3(4):29\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eAbraham J. International Conference On Harmonisation Of Technical Requirements For Registration Of Pharmaceuticals For Human Use. In: Tietje C, Brouder A, editors. Handbook of Transnational Economic Governance Regimes [Internet]. Brill | Nijhoff; 2010 [cited 2024 Sep 2]. p. 1041\u0026ndash;53. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://brill.com/view/book/edcoll/9789004181564/Bej.9789004163300.i-1081_085.xml\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eGhesqui\u0026egrave;res H, Rossi C, Cherblanc F, Le Guyader-Peyrou S, Bijou F, Sujobert P et al. A French multicentric prospective prognostic cohort with epidemiological, clinical, biological and treatment information to improve knowledge on lymphoma patients: study protocol of the \"REal world dAta in LYmphoma and survival in adults\" (REALYSA) cohort. BMC Public Health. 2021;21(1):432.\u003c/li\u003e\n\u003cli\u003eXu S, Ross C, Raebel MA, Shetterly S, Blanchette C, Smith D. Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals. Value Health. 2010;13(2):273\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eShiba K, Kawahara T. Using Propensity Scores for Causal Inference: Pitfalls and Tips. J Epidemiol. 2021;31(8):457\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eLeyrat C, Seaman SR, White IR, Douglas I, Smeeth L, Kim J, et al. Propensity score analysis with partially observed covariates: How should multiple imputation be used? Stat Methods Med Res. 2019;28(1):3\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eNguyen TQ, Stuart EA. Multiple imputation for propensity score analysis with covariates missing at random: some clarity on \u0026ldquo;within\u0026rdquo; and \u0026ldquo;across\u0026rdquo; methods. American Journal of Epidemiology. 2024;193(10):1470\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eGhesqui\u0026egrave;res H, Cherblanc F, Belot A, Micon S, Bouabdallah KK, Esnault C, et al. Challenges for quality and utilization of real-world data for diffuse large B-cell lymphoma in REALYSA, a LYSA cohort. Blood Adv. 2024;8(2):296\u0026ndash;308.\u003c/li\u003e\n\u003cli\u003eYin X, Stuart E, Burcu M, Stewart M, Lamont E, Davi R. The Validity of a Synthetic Control Arm Derived from Historical Multiple Myeloma Clinical Trials. Blood. 2023;142(Supplement 1):6628.\u003c/li\u003e\n\u003cli\u003eVan Le H, Van Naarden Braun K, Nowakowski GS, Sermer D, Radford J, Townsend W, et al. Use of a real-world synthetic control arm for direct comparison of lisocabtagene maraleucel and conventional therapy in relapsed/refractory large B-cell lymphoma. Leuk Lymphoma. 2023;64(3):573\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eRossi C, Marouf A, Deau Fischer B, Cherblanc F, Cugnod E, Chartier L, et al. First Line Therapy Evaluation Using Propensity Score Approach in Newly Diagnosed Advanced Classical Hodgkin Lymphoma Patients from Prospective Real-World Realysa Cohort and Phase 3 AHL2011 Trial. Blood. 2023;142:3056.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\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":"blood-cancer-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bcj","sideBox":"Learn more about [Blood Cancer Journal](http://www.nature.com/bcj/)","snPcode":"41408","submissionUrl":"https://mts-bcj.nature.com/cgi-bin/main.plex","title":"Blood Cancer Journal","twitterHandle":"@bloodcancerjnl","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6822758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6822758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePatients over 80 years (\u0026ge;\u0026thinsp;80 y.o) with newly diagnosed diffuse large B-cell lymphomas (DLBCL) are underrepresented in clinical trials (CT). Use of synthetic control arms (SCA) could help to build innovative comparatives trials. With this analysis, we aim to demonstrate the clinical performance \u0026amp; use of such SCA. With data from both CT (LNH09-7B) and real-world (REALYSA), we built a mixed SCA composed of \u0026ge;\u0026thinsp;80 y.o patients with DLBCL in first line of treatment. In order to display clinically meaningful results, we demonstrate how we can reproduce SENIOR results, a double-arm randomized CT (RCT), by switching the internal control arm by our newly built SCA. Patients between arms were balanced using stabilized inverse probability of treatment weighting approach based on propensity scores (PS) and the endpoint was overall survival (OS). All covariates included in PS were well balanced after weighting, and OS of Mixed SCA \u003cem\u003evs.\u003c/em\u003e SENIOR experimental arm were not statistically different, with a HR of 0.743 [0.494\u0026ndash;1.118] (p\u0026thinsp;=\u0026thinsp;0.1654). Use of SCA built only from real-world data (REALYSA) and sensitivity analyses using different missing data management methods didn\u0026rsquo;t differ from the whole analysis. In newly diagnosed elderly DLBCL patients, the use of SCAs can mimic the control arm of a RCT and could be used to build comparative CT for elderly patients and come-up with fast-innovative CTs.\u003c/p\u003e","manuscriptTitle":"Synthetic control arm from mixed clinical trials and real-world data from LYSA group for untreated diffuse large B cell lymphoma patients aged over 80 years: a bona fide strategy for innovative clinical trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 15:07:51","doi":"10.21203/rs.3.rs-6822758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-07-08T11:27:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-03T14:23:46+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-18T15:54:55+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-18T15:11:20+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-13T16:45:36+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-06-11T12:04:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T11:20:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T11:18:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Blood Cancer Journal","date":"2025-06-09T22:19:22+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-06-05T11:20:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"blood-cancer-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bcj","sideBox":"Learn more about [Blood Cancer Journal](http://www.nature.com/bcj/)","snPcode":"41408","submissionUrl":"https://mts-bcj.nature.com/cgi-bin/main.plex","title":"Blood Cancer Journal","twitterHandle":"@bloodcancerjnl","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f52b187-6572-466d-8078-d15df9c1dcd8","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49885059,"name":"Health sciences/Medical research/Clinical trial design/Clinical trials"},{"id":49885060,"name":"Health sciences/Diseases/Haematological diseases/Haematological cancer/Lymphoma/Non-hodgkin lymphoma/B-cell lymphoma"}],"tags":[],"updatedAt":"2025-11-04T08:08:34+00:00","versionOfRecord":{"articleIdentity":"rs-6822758","link":"https://doi.org/10.1038/s41408-025-01374-x","journal":{"identity":"blood-cancer-journal","isVorOnly":false,"title":"Blood Cancer Journal"},"publishedOn":"2025-11-03 05:00:00","publishedOnDateReadable":"November 3rd, 2025"},"versionCreatedAt":"2025-06-16 15:07:51","video":"","vorDoi":"10.1038/s41408-025-01374-x","vorDoiUrl":"https://doi.org/10.1038/s41408-025-01374-x","workflowStages":[]},"version":"v1","identity":"rs-6822758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6822758","identity":"rs-6822758","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}