array(40) {
  ["project_status"]=>
  string(7) "ongoing"
  ["project_assoc_trials"]=>
  array(1) {
    [0]=>
    object(WP_Post)#4157 (24) {
      ["ID"]=>
      int(14193)
      ["post_author"]=>
      string(4) "1638"
      ["post_date"]=>
      string(19) "2024-02-15 13:54:24"
      ["post_date_gmt"]=>
      string(19) "2024-02-15 18:54:24"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(252) "NCT01776840 - A Randomized, Double-blind, Placebo-controlled Phase 3 Study of the Bruton's Tyrosine Kinase (BTK) Inhibitor, PCI-32765 (Ibrutinib), in Combination With Bendamustine and Rituximab (BR) in Subjects With Newly Diagnosed Mantle Cell Lymphoma"
      ["post_excerpt"]=>
      string(0) ""
      ["post_status"]=>
      string(7) "publish"
      ["comment_status"]=>
      string(6) "closed"
      ["ping_status"]=>
      string(6) "closed"
      ["post_password"]=>
      string(0) ""
      ["post_name"]=>
      string(187) "nct01776840-a-randomized-double-blind-placebo-controlled-phase-3-study-of-the-brutons-tyrosine-kinase-btk-inhibitor-pci-32765-ibrutinib-in-combination-with-bendamustine-and-rituximab-br-i"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-09-30 10:16:35"
      ["post_modified_gmt"]=>
      string(19) "2025-09-30 14:16:35"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(60) "https://yoda.yale.edu/?post_type=clinical_trial&p=14193"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
  }
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["project_title"]=>
  string(148) "COMPARE-MCL: Cross-Trial Comparison of MCL Treatments in older patients – A Pooled analysis of MCL Elderly, SHINE, MCL R2 Elderly, ENRICH and ECHO"
  ["project_narrative_summary"]=>
  string(860) "Over the past two decades, several novel targeted therapies have been introduced for the treatment of mantle cell lymphoma (MCL).(1) Although many clinical trials have investigated novel induction or maintenance therapies in older, previously untreated patients, none of them have been directly evaluated against each other. Furthermore, previous comparisons of two widely used immunochemotherapy standards yielded conflicting results and were limited by small sample sizes and heterogeneous patient populations.(2–5) To address these knowledge gaps, we will use individual patient data from the MCL Elderly(6), SHINE(7), MCL R2 Elderly(8), ENRICH(9) and ECHO(10) trials, to perform efficacy and safety comparisons of novel established first-line treatment elements applying inverse probability of treatment weighting  to control confounding.



"
  ["project_learn_source"]=>
  string(11) "data_holder"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(3) "Eva"
    ["last_name"]=>
    string(6) "Hoster"
    ["degree"]=>
    string(44) "Univ. Prof. Dr. rer. biol. hum., Dipl.-Math."
    ["primary_affiliation"]=>
    string(90) "Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), LMU Munich"
    ["email"]=>
    string(31) "ehoster@ibe.med.uni-muenchen.de"
    ["state_or_province"]=>
    string(7) "Bavaria"
    ["country"]=>
    string(7) "Germany"
  }
  ["project_key_personnel"]=>
  array(3) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Katja"
      ["p_pers_l_name"]=>
      string(7) "Gutmair"
      ["p_pers_degree"]=>
      string(4) "MSc."
      ["p_pers_pr_affil"]=>
      string(111) "Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, LMU Munich"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Alicia"
      ["p_pers_l_name"]=>
      string(11) "Duran Egana"
      ["p_pers_degree"]=>
      string(4) "BSc."
      ["p_pers_pr_affil"]=>
      string(111) "Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, LMU Munich"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Martin"
      ["p_pers_l_name"]=>
      string(8) "Dreyling"
      ["p_pers_degree"]=>
      string(14) "Prof. Dr. med."
      ["p_pers_pr_affil"]=>
      string(52) " Department of Medicine III, LMU University Hospital"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1681) "Background: 

Mantle cell lymphoma (MCL) is a rare, incurable subtype of B-cell non-Hodgkin lymphoma. Over the past decades, novel targeted therapies have been introduced for the treatment of MCL. Different BTKi regimens with and without chemotherapy and maintenance including lenalidomide have been shown to be superior to standard first-line treatment in older patients. 

Objective: 

The objective is to compare the efficacy and safety of treatment strategies involving novel agents that have not yet been directly studied against each other. A further goal is to confirm the superiority of BR induction over R-CHOP. 



Study Design

This is a pooled individual patient data analysis across five large phase 3 randomized clinical trials MCL Elderly, SHINE, MCL R2 Elderly, ENRICH and ECHO. 

Participants

The population of interest are older, previously untreated, transplant-ineligible patients with histopathologically confirmed MCL, ECOG ≤2, and stage II–IV. 



Primary and Secondary Outcome Measure(s)

The primary outcome is failure-free survival. Secondary outcomes are time to next treatment, overall survival, complete and overall response after induction, Adverse Events.



Statistical Analysis

We will adjust for confounders using inverse probability of treatment weighted Kaplan-Meier curves and weighted log-rank tests. Hazard ratios will be estimated with weighted Cox or Poisson regression, depending on the proportional hazards. Response rates will be analyzed using weighted logistic regression. All hypothesis tests will use a two-sided 5% significance level.

"
  ["project_brief_bg"]=>
  string(3209) "Mantle cell lymphoma (MCL) is a rare subtype of B-cell non-Hodgkin lymphoma with a heterogenous clinical behavior(11). MCL is still incurable with poor long-term prognosis, especially for older patients(12). In the last two decades, novel, targeted therapies have been introduced, among others the BTKi inhibitors ibrutinib and acalabrutinib and lenalidomide(1,13). While ibrutinib, acalabrutinib and lenalidomide are already approved for the use in relapsed/refractory older patients(13), there are no approvals as first-line therapy yet. However, those drugs have been shown to be superior to previous treatment standards or are still under investigation on phase III trials. 

In MCL Elderly, a randomized, open-label multicenter phase III trial, patients were randomized to either induction R-CHOP or R-FC. Patients responding to the induction therapy were randomized to either rituximab maintenance (RM) or Interferon-alpha. Remission rates after induction were similar, but R-FC was more toxic. RM was superior in terms of PFS compared to Interferon-alpha. In the international, randomized, double-blind, phase III SHINE trial, the addition of ibrutinib to both Bendamustine-rituximab (BR) induction as well as to RM prolonged PFS(7). The MCL R2 Elderly trial compared R-CHOP vs. R-CHOP/R-HAD induction and, in responders, rituximab vs. lenalidomide + rituximab maintenance. While induction outcomes were similar, lenalidomide + rituximab improved PFS(8).  ENRICH was the first randomized, open-label phase II/III trial that compared Rituximab + Ibrutinib as chemotherapy-free induction followed by ibrutinib-rituximab maintenance to chemotherapy (either R-CHOP or BR, stratified at randomization). There was a significant improvement in PFS of the ibrutinib-containing regimen(9). ECHO, a phase III, multicenter, double-blind, placebo-controlled trial showed, that the addition of acalabrutinib to the induction bendamustine-rituximab (BR-A) and to RM significantly improved PFS(10). 

Till now, these superior novel treatment regimens have not been directly evaluated against each other. The detailed treatment regimens which we want to compare are described in the next chapter.



Another open question is the confirmation of results from several trials indirectly suggesting the superiority of BR as induction compared to R-CHOP as induction. While the ENRICH trial indicates better outcomes with BR, it did not provide a direct comparison between the two chemotherapy-based regimens, as it was not randomized for this purpose, but stratified on a per-patient-level, subject to confounding bias.  Similarly, the STiL and BRIGHT trials reported improved efficacy of BR; however, the mantle cell lymphoma (MCL) subgroups in these studies were small(3,4) A separate pooled analysis comparing R-CHOP and BR also found no significant difference in outcomes, though this comparison was limited by the use of a clinical trial population (from the MCL Elderly trial) versus a population-based cohort(2). To address these limitations, we will combine data on BR and R-CHOP induction regimens from multiple trials (the previously mentioned SHINE, MCL R2 Elderly, ENRICH, ECHO.

"
  ["project_specific_aims"]=>
  string(1709) "Abbreviations: 

•	R-CHOP: Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone

•	RB: Rituximab, Bendamustine

•	R-HAD: rituximab, cytarabine, Dexamethason

•	 R2: Rituximab, Lenalidomide



The objectives are:



1.	To assess whether novel targeted therapies without chemotherapy- regimens improves clinical outcomes without increased toxicity compared to novel targeted therapies with chemotherapy: 

i.	Rituximab-Ibrutinib + Rituximab-Ibrutinib (ENRICH) vs. RB - Ibrutinib+ Rituximab-Ibrutinib (SHINE) 

ii.	Rituximab-Ibrutinib + Rituximab-Ibrutinib (ENRICH) vs. RB-Acalabrutinib + Rituximab - Acalabrutinib (ECHO) 

iii.	Rituximab-Ibrutinib + Rituximab-Ibrutinib (ENRICH)  vs. R-CHOP + R2 (R2 Elderly) 

2.	To compare the efficacy and safety of treatment regimens with different novel, targeted therapies combined with chemotherapy:

i.	RB - Ibrutinib + Rituximab - Ibrutinib (SHINE) vs. RB-Acalabrutinib + Rituximab - Acalabrutinib (ECHO)

ii.	RB - Ibrutinib + Rituximab - Ibrutinib (SHINE) vs. R-CHOP + R2 (R2 Elderly)

iii.	RB-Acalabrutinib + Rituximab - Acalabrutinib (ECHO) vs. R-CHOP + R2 (R2 Elderly)

3.	To compare the efficacy and safety of lenalidomide-containing maintenance and BTKi-containing maintenance: 

i.	R2 (after R-CHOP, R2 Elderly) vs. R-I (after BR-I, SHINE or after R-I, ENRICH)

ii.	R2 (after R-CHOP, R2 Elderly) vs. R-A (after BR-A, ECHO)

4.	To confirm the superiority of BR + rituximab maintenance (ECHO, SHINE, ENRICH) against R-CHOP + rituximab maintenance (MCL Elderly, MCL R2 Elderly, ENRICH) in terms of efficacy and safety.

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
  }
  ["project_purposes"]=>
  array(5) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(49) "new_research_question_to_examine_treatment_safety"
      ["label"]=>
      string(49) "New research question to examine treatment safety"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [4]=>
    array(2) {
      ["value"]=>
      string(56) "participant_level_data_meta_analysis_from_yoda_and_other"
      ["label"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
    }
  }
  ["project_research_methods"]=>
  string(1248) "We have access to our own data of the MCL Elderly trial. The data of MCL R2 Elderly, ECHO, and ENRICH will be shared based on scientific collaborations. The inclusion and exclusion criteria of the MCL Elderly trial, MCL R2 Elderly trial, ECHO, SHINE, and ENRICH were largely comparable, with only minor differences(6–10). To ensure similar trial populations across studies, we will apply the following standardized inclusion criteria: previously untreated, with histopathologically confirmed MCL, an ECOG performance status of 0–2, Ann Arbor stage II–IV disease, and radiologically measurable lesions (>1.5 cm). Key exclusion criteria included planned autologous stem cell transplantation and central nervous system (CNS) involvement. Since SHINE was the only trial with an ECOG 0-1 as inclusion criteria, we will adapt the inclusion criteria of the other trials accordingly when comparing them with arms of the SHINE trial. Furthermore, ENRICH, MCL Elderly and MCL R2 Elderly had as inclusion criteria age >=60, whereas the other trials had as inclusion criteria age >=60. When we compare treatment arms of the respective clinical trials with those different age inclusion criteria, we well adapt the inclusion criteria accordingly."
  ["project_main_outcome_measure"]=>
  string(2079) "Primary endpoints:

•	Comparing the whole treatment regimens is failure-free survival (FFS). FFS is defined as time from (first) randomization to date of stable disease at end of induction therapy, first progression/relapse, or date of death from any cause, whichever came first. Patients without any evaluable staging results are censored one day after randomization. Patients who were alive without treatment failure/progression/relapse at date of the last follow up, are censored at the date of the last contact without treatment failure/progression/relapse.

•	Comparing only the maintenance therapies is Progression-free survival (PFS): PFS is defined as time from staging after end induction to first progression/relapse or death from any cause. Patients without evaluable staging results are censored one day after end of induction. Patients alive without progression/relapse at date of the last follow up are censored at the last contact day without progression. 



Secondary endpoints: 

•	Overall survival (OS) is defined as time from randomization/time from staging after end of induction to the date of death from any cause. Patients, who were alive at date of the last follow up, are censored at the last contact date alive.

•	Time to next treatment (TNT) will be defined as time from randomization/time from staging after end of induction to start of the next line therapy. Death is counted as competing event. Patients, in whom no further treatment has been started, are censored at the last contact date alive.

•	Complete response after end of induction

•	Adverse event by System Organ Class and Preferred Term.

•	Time to hematological and non-hematological secondary malignancy is defined as time from (first) randomization to a documented secondary primary hematological and non-hematological malignancy, respectively. Patients without secondary malignancy will be censored at the last contact date alive. Death without a secondary malignancy is a competing event.





"
  ["project_main_predictor_indep"]=>
  string(1203) "We will compare different treatment regimens from the MCL Elderly(6), SHINE(7), MCL R2 Elderly(8), ENRICH(9) and ECHO(10) trials. All pairwise comparisons are listed under “3. Specific Aims of the Project”. For details on drug dosing, number of cycles, and treatment timing, please refer to the original publications and the supplemental file, as space constraints and character limits prevent inclusion here (6–10).

Patients will be evaluated according to the treatment they had been randomized to irrespective of treatment discontinuation, dose modifications, concomitant medication, or salvage therapy before relapse (treatment policy strategy to handle intercurrent events under the ICH E9 Estimand framework, corresponding to intention-to-treat analyses). Furthermore, we will evaluate the efficacy under the hypothetical setting if no patients had discontinued treatment outside of what was specified in the protocol—that is, only protocol-defined treatment interruptions and dose adjustments are permitted. This is particularly relevant for clinicians and patients who are interested in understanding the treatment effect when the regimen is followed as prescribed.



"
  ["project_other_variables_interest"]=>
  string(2274) "Baseline variables as well as variables measured after end of induction/start of maintenance for the sample characterization and confounder adjustment are needed to adjust the treatment effect for confounding. Additionally, it would be valuable to have these variables recorded for each visit, if available, as they are required for the supplemental analysis estimating the treatment effect under protocol-compliant dosing. 

•	Age in years at (first) randomization (continuous)

•	Sex (binary male/female)

•	Histology (categorical: classical, small cell, blastoid, pleomorph)

•	Ann Arbor Stage at baseline visit (categorical)

•	B-symptoms at baseline visit (categorical)

•	Eastern Cooperative Oncology group (ECOG) performance status at randomization (categorical)

•	LDH/ULDH ratio (continuous) and LDH ≥ ULDH (categorical) at randomization

•	White blood cell count (WBC, G/L) at randomization (continuous)

•	MCL International Prognostic Index (MIPI) score (continuous) and MIPI group (categorical)

•	Ki-67 index (continuous in % and binary with cut-off 30%) 

•	Cytology (categorical: classical, small cell, blastoid, pleomorph)

•	p53 expression (categorical, >50% und <=50%) 



Variables needed to calculate the endpoints:

•	Date of randomization 

•	Date of start and end of induction

•	Date of staging after induction and its staging result 

•	Date of start and end of maintenance therapy

•	Date of first progression

•	Date of death and cause of death

•	Last contact date without progression

•	Last contact date

•	Date of start of next treatment or salvage treatment and which salvage treatment

•	Date of Adverse event and its System Organ Class and Preferred Term and grade

•	Date and type of second malignancies



Other variables: 

•	Dose of drug at every visit + date of visit, 

•	Date of permanently/ intercurrent discontinuation 

•	Reasons for a dose reduction and interruption of the treatment 

•	Reasons for study withdrawal

•	Start and end date of treatment interruption 

"
  ["project_stat_analysis_plan"]=>
  string(5035) "Baseline characteristics will be described using absolute and relative frequencies for categorial variables and median and first and third quantiles for continuous variables. Missing values in baseline variables will be multiple imputed with multivariate imputation of chained equations (MICE)(14). 

MCL Elderly and MCL R2 Elderly had a factorial design with a second randomization after end of induction of all those patients, who responded to the induction therapy. To prevent selection bias by conditioning the comparison of the whole treatment regime on only those patients who had responded to the induction therapy, we randomly assign those patients not having responded to the induction therapy to the maintenance therapies using the block randomization strategy as those used in the respective trials.

We will calculate confounder adjusted survival curves using the Kaplan-Meier estimator with stabilized inverse probability of treatment weighting (IPTW)(15). The IPTW ensures confounder balance between the treatment groups by reweighting patients based on their inverse probability of receiving the treatment they actually received, conditional on confounding variables. We will calculate the propensity score (PS) with a logistic regression with treatment groups as dependent variable and MIPI single variables (= age, Eastern Cooperative Oncology Group (ECOG) Performance Status, white blood cells (WBC), Lactate Dehydrogenase (LDH)/upper limit LDH), Ki67, cytology and p53 at baseline as independent variables. The Survival curves will be compared with a weighted log-rank test(15). The number to treat at clinically relevant endpoints will be calculated using the survival probabilities from the Kaplan-Meier curves. We will calculate the hazard ratios using a IPTW weighted Cox regression. In case of non-proportional hazards, we will use a Poisson regression to model the hazard ratio as a function of time. 

As sensitivity analysis, to account for potential informative censoring, we will additionally to the IPTW calculate inverse probability of censoring weights (IPCW)(16). The IPCW is the inverse probability of being uncensored and is calculated with a logistic regression incorporating those variables, that are associated with the endpoint as well as with the loss-to-follow up: MIPI single variables, Ki67, cytology, p53, Ann-Arbor stage, treatment arm. Furthermore, will calculate the survival curves and the corresponding cumulative incidence risks of a progress/death or death alone using a pooled regression(= iterated conditional expectation estimator)(17–19). The sequential g-formula estimates the cumulative incidence risk at a specific time if everyone had received the respective treatment. The 95% CIs will be obtained with bootstrapping.

Death is a competing event for time to next treatment. We will compute the total effect by calculating the IPTW weighted Aalen–Johansen estimator of the cumulative incidence function (CIF) separately for next treatment and death stratified by treatment(20,21). Differences cumulative incidence curves will be tested for a difference using bootstrapping-based p-values based on the area under the curve(22).  Furthermore, we will calculate the cumulative incidence difference and its corresponding 95% confidence interval at clinical relevant endpoints(20). As sensitivity analysis, we will also calculate cumulative incidence curves using the g formula based on pooled, logistic regressions(21). 

For calculating the absolute risk difference of overall and complete response after end of induction, we will use a weighted logistic regression. The treatment arm and baseline MIPI single variables, Ki67, cytology and p53 and possible interactions will be included in the logistic regression. Patients without a missing staging result/who were lost to follow-up will be excluded. In a sensitivity analysis, we will use IPCW.

For the analysis of safety outcomes, the absolute and relative frequencies of patients with at least one grade 3-5 AE and grade 5 AE per treatment group will be described separately for the induction and maintenance therapy by System Organ Class and/or preferred term. For time to secondary hematological malignancy and non-hematological malignancy competing risk cumulative incidence curves will be calculated and compared with the Gray’s test.

As supplemental analysis, we will calculate the survival curves and survival differences at clinically relevant timepoints under the hypothetical setting, if no patients had discontinued treatment or had a dose reduction outside of what was specified in the protocol. We will censor patients at the timepoint when they had an interruption of the treatment (or a dose reduction) outside from the protocol. We will use an inverse probability of adherence weighted marginal structural model to account for a potential selection bias introduced by censoring those patients at that timepoint when did not adhere to the treatment anymore(23)."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(1367) "Project Start: Upon receipt of at least one additional datasets to the already available MCL Elderly data (earliest November 2025)

Phase 1 – Writing a statistical analysis plan

Estimated Duration: 2-4 weeks

Phase 2 – Data cleaning and harmonization

•	Pool data from five clinical trials (harmonize variable names, check for plausibility and completeness)

•	Calculation of endpoints and relevant scores

•	Harmonize inclusion criteria for the pairwise comparisons

Estimated Duration: 6–8 weeks

Phase 3 – Statistical analyses

•	Descriptive statistics: Table 1, frequency of missing values, density function of baseline variables

•	Analysis of efficacy and safety for all 9 pairwise comparisons as outlined in the statistical section

•	Generate tables and figures for all comparisons

Estimated Duration: 14–17 weeks



Phase 4 – Statistical analysis report and manuscript drafting

•	Include results into the statistical analysis plan and report

•	Draft manuscript including methods, results, figures, discussion

•	Internal review and revisions

Estimated Duration: 6–9 weeks



Phase 6 – Manuscript Submission (s) and Reporting Results to YODA Project

Estimated Duration: 3 weeks



"
  ["project_dissemination_plan"]=>
  string(209) "We plan 2-3 publications in highly ranked hematological or oncological journals like JCO, Lancet Haematology, Blood. The target audience are clinically active physicians and clinical practice guideline authors"
  ["project_bibliography"]=>
  string(5145) "

Gribbin C, Chen J, Martin P, Ruan J. Novel treatment for mantle cell lymphoma – impact of BTK inhibitors and beyond. Leukemia & Lymphoma. 2024;65(1):1–13.
Gutmair K, Villa D, Cunningham N, Silkenstedt E, Rimsza L, Ramsower C, u. a. BR or R-CHOP induction with rituximab maintenance in untreated, transplant-ineligible patients with mantle cell lymphoma. Blood Advances. Mai 2025;9(9):2302–6.
Flinn IW, van der Jagt R, Kahl B, Wood P, Hawkins T, MacDonald D, u. a. First-Line Treatment of Patients With Indolent Non-Hodgkin Lymphoma or Mantle-Cell Lymphoma With Bendamustine Plus Rituximab Versus R-CHOP or R-CVP: Results of the BRIGHT 5-Year Follow-Up Study. Journal of Clinical Oncology. 2019;37(12):984–91.
Rummel MJ, Maschmeyer G, Ganser A, Heider A, von Gruenhagen U, Losem C, u. a. Bendamustine plus rituximab (B-R) versus CHOP plus rituximab (CHOP-R) as first-line treatment in patients with indolent lymphomas: Nine-year updated results from the StiL NHL1 study. Journal of Clinical Oncology. 2017;35(15_suppl):7501–7501.
Villa D, Sehn LH, Savage KJ, Toze CL, Song K, Brok WD den, u. a. Bendamustine and rituximab as induction therapy in both transplant-eligible and -ineligible patients with mantle cell lymphoma. Blood Advances. 2020;4(15):3486–94.
Kluin-Nelemans HC, Hoster E, Hermine O, Walewski J, Trneny M, Geisler CH, u. a. Treatment of Older Patients with Mantle-Cell Lymphoma. New England Journal of Medicine. 2012;367(6):520–31.
Wang ML, Jurczak W, Jerkeman M, Trotman J, Zinzani PL, Belada D, u. a. Ibrutinib plus Bendamustine and Rituximab in Untreated Mantle-Cell Lymphoma. New England Journal of Medicine. 2022;386(26):2482–94.
Ribrag V, Safar V, Kluin-Nelemans H, Oberic L, Feugier P, Casasnovas O, u. a. Induction and Maintenance Therapy in Elderly Patients with Mantle Cell Lymphoma: Double-Randomized MCL R2 Elderly Clinical Trial By the European Mantle Cell Lymphoma Network. Blood. November 2023;142(Supplement 1):979–979.
Lewis DJ, Jerkeman M, Sorrell L, Wright D, Glimelius I, Pasanen A, u. a. Ibrutinib-Rituximab Is Superior to Rituximab-Chemotherapy in Previously Untreated Older Mantle Cell Lymphoma Patients: Results from the International Randomised Controlled Trial, Enrich. Blood. 5. November 2024;144(Supplement 1):235–235.
Wang M, Salek D, Belada D, Song Y, Jurczak W, Kahl BS, u. a. Acalabrutinib Plus Bendamustine-Rituximab in Untreated Mantle Cell Lymphoma. Journal of Clinical Oncology. 2025;43(20):2276–84.
Jain P, Wang ML. Mantle cell lymphoma in 2022—A comprehensive update on molecular pathogenesis, risk stratification, clinical approach, and current and novel treatments. American Journal of Hematology. 2022;97(5):638–56.
Romancik JT, Cohen JB. Management of Older Adults with Mantle Cell Lymphoma. Drugs & Aging. 1. Juli 2020;37(7):469–81.
Owen C, Berinstein NL, Christofides A, Sehn LH. Review of Bruton Tyrosine Kinase Inhibitors for the Treatment of Relapsed or Refractory Mantle Cell Lymphoma. Current Oncology. 2019;26(2):233–40.
White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine. 2011;30(4):377–99.
Xie J, Liu C. Adjusted Kaplan–Meier estimator and log-rank test with inverse probability of treatment weighting for survival data. Statistics in Medicine. 2005;24(20):3089–110.
Willems SJW, Schat A, Noorden M van, Fiocco M. Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator. Statistical Methods in Medical Research. 2018;27(2):323–35.
Naimi AI, Cole SR, Kennedy EH. An introduction to g methods. International Journal of Epidemiology. Dezember 2016;46(2):756–62.
Taubman SL, Robins JM, Mittleman MA, Hernán MA. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. International Journal of Epidemiology. April 2009;38(6):1599–611.
Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC. Methods for dealing with time-dependent confounding. Statistics in Medicine. 2013;32(9):1584–618.
Austin PC, Fine JP. Inverse Probability of Treatment Weighting Using the Propensity Score With Competing Risks in Survival Analysis. Statistics in Medicine. 28. Februar 2025;44(5):e70009.
Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A causal framework for classical statistical estimands in failure-time settings with competing events. Statistics in Medicine. 2020;39(8):1199–236.
Pepe MS, Fleming TR. Weighted Kaplan-Meier Statistics: A Class of Distance Tests for Censored Survival Data. Biometrics. 1989;45(2):497–507.

23.         Murray EJ, Caniglia EC, Petito LC. Causal survival analysis: A guide to estimating intention-to-treat and per-protocol effects from randomized clinical trials with non-adherence. Research Methods in Medicine & Health Sciences. 2021;2(1):39–49.
"
  ["project_suppl_material"]=>
  array(2) {
    [0]=>
    array(1) {
      ["suppl_file"]=>
      array(21) {
        ["ID"]=>
        int(17968)
        ["id"]=>
        int(17968)
        ["title"]=>
        string(17) "supplemental.docx"
        ["filename"]=>
        string(17) "supplemental.docx"
        ["filesize"]=>
        int(39379)
        ["url"]=>
        string(66) "https://yoda.yale.edu/wp-content/uploads/2025/09/supplemental.docx"
        ["link"]=>
        string(63) "https://yoda.yale.edu/data-request/2025-0528/supplemental-docx/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2160"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(17) "supplemental-docx"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17632)
        ["date"]=>
        string(19) "2025-09-26 15:57:15"
        ["modified"]=>
        string(19) "2025-09-26 15:57:25"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(71) "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(59) "vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [1]=>
    array(1) {
      ["suppl_file"]=>
      array(21) {
        ["ID"]=>
        int(18116)
        ["id"]=>
        int(18116)
        ["title"]=>
        string(59) "YODA Project Research Proposal Direct Data Access 2025-0528"
        ["filename"]=>
        string(63) "YODA-Project-Research-Proposal-Direct-Data-Access-2025-0528.pdf"
        ["filesize"]=>
        int(203141)
        ["url"]=>
        string(112) "https://yoda.yale.edu/wp-content/uploads/2025/07/YODA-Project-Research-Proposal-Direct-Data-Access-2025-0528.pdf"
        ["link"]=>
        string(105) "https://yoda.yale.edu/data-request/2025-0528/yoda-project-research-proposal-direct-data-access-2025-0528/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(2) "20"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(59) "yoda-project-research-proposal-direct-data-access-2025-0528"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17632)
        ["date"]=>
        string(19) "2025-10-16 15:01:29"
        ["modified"]=>
        string(19) "2025-10-16 15:01:29"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
  }
  ["project_coi"]=>
  array(4) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17969)
        ["id"]=>
        int(17969)
        ["title"]=>
        string(19) "COI_-Eva_hoster.pdf"
        ["filename"]=>
        string(19) "COI_-Eva_hoster.pdf"
        ["filesize"]=>
        int(20824)
        ["url"]=>
        string(68) "https://yoda.yale.edu/wp-content/uploads/2025/09/COI_-Eva_hoster.pdf"
        ["link"]=>
        string(65) "https://yoda.yale.edu/data-request/2025-0528/coi_-eva_hoster-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2160"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(19) "coi_-eva_hoster-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17632)
        ["date"]=>
        string(19) "2025-09-26 15:57:15"
        ["modified"]=>
        string(19) "2025-09-26 15:57:25"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [1]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17970)
        ["id"]=>
        int(17970)
        ["title"]=>
        string(21) "COI_Katja_Gutmair.pdf"
        ["filename"]=>
        string(21) "COI_Katja_Gutmair.pdf"
        ["filesize"]=>
        int(20256)
        ["url"]=>
        string(70) "https://yoda.yale.edu/wp-content/uploads/2025/09/COI_Katja_Gutmair.pdf"
        ["link"]=>
        string(67) "https://yoda.yale.edu/data-request/2025-0528/coi_katja_gutmair-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2160"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(21) "coi_katja_gutmair-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17632)
        ["date"]=>
        string(19) "2025-09-26 15:57:18"
        ["modified"]=>
        string(19) "2025-09-26 15:57:25"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [2]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18113)
        ["id"]=>
        int(18113)
        ["title"]=>
        string(29) "CIO_martin_dreyling_no_PI.pdf"
        ["filename"]=>
        string(29) "CIO_martin_dreyling_no_PI.pdf"
        ["filesize"]=>
        int(31632)
        ["url"]=>
        string(78) "https://yoda.yale.edu/wp-content/uploads/2025/10/CIO_martin_dreyling_no_PI.pdf"
        ["link"]=>
        string(75) "https://yoda.yale.edu/data-request/2025-0528/cio_martin_dreyling_no_pi-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2160"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(29) "cio_martin_dreyling_no_pi-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17632)
        ["date"]=>
        string(19) "2025-10-15 07:26:11"
        ["modified"]=>
        string(19) "2025-10-15 07:26:12"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [3]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17972)
        ["id"]=>
        int(17972)
        ["title"]=>
        string(14) "COI-ADuran.pdf"
        ["filename"]=>
        string(14) "COI-ADuran.pdf"
        ["filesize"]=>
        int(20718)
        ["url"]=>
        string(63) "https://yoda.yale.edu/wp-content/uploads/2025/09/COI-ADuran.pdf"
        ["link"]=>
        string(60) "https://yoda.yale.edu/data-request/2025-0528/coi-aduran-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2160"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(14) "coi-aduran-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17632)
        ["date"]=>
        string(19) "2025-09-26 15:57:23"
        ["modified"]=>
        string(19) "2025-09-26 15:57:25"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
  }
  ["data_use_agreement_training"]=>
  bool(true)
  ["human_research_protection_training"]=>
  bool(true)
  ["certification"]=>
  bool(true)
  ["search_order"]=>
  string(1) "0"
  ["project_send_email_updates"]=>
  bool(false)
  ["project_publ_available"]=>
  bool(true)
  ["project_year_access"]=>
  string(4) "2026"
  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  array(21) {
    ["ID"]=>
    int(18590)
    ["id"]=>
    int(18590)
    ["title"]=>
    string(47) "YODA Project Due Diligence Assessment 2025-0528"
    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2025-0528.pdf"
    ["filesize"]=>
    int(106819)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2025/07/YODA-Project-Due-Diligence-Assessment-2025-0528.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2025-0528/yoda-project-due-diligence-assessment-2025-0528/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2025-0528"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17632)
    ["date"]=>
    string(19) "2026-01-14 17:14:42"
    ["modified"]=>
    string(19) "2026-01-14 17:14:42"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(18591)
    ["id"]=>
    int(18591)
    ["title"]=>
    string(46) "YODA Project Protocol - 2025-0528 - 2025-10-16"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0528-2025-10-16.pdf"
    ["filesize"]=>
    int(139714)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/07/YODA-Project-Protocol-2025-0528-2025-10-16.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0528/yoda-project-protocol-2025-0528-2025-10-16/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0528-2025-10-16"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17632)
    ["date"]=>
    string(19) "2026-01-14 17:15:06"
    ["modified"]=>
    string(19) "2026-01-14 17:15:06"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(18592)
    ["id"]=>
    int(18592)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0528_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0528_site.pdf"
    ["filesize"]=>
    int(1332032)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/07/YODA-Project-Review-2025-0528_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0528/yoda-project-review-2025-0528_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0528_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17632)
    ["date"]=>
    string(19) "2026-01-14 17:15:36"
    ["modified"]=>
    string(19) "2026-01-14 17:15:36"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_overridden_res"]=>
  string(1) "3"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(1) {
  [0]=>
  string(9) "imbruvica"
}


num of trials
array(1) {
  [0]=>
  string(1) "1"
}


res
array(1) {
  [0]=>
  string(1) "3"
}