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      ["post_title"]=>
      string(346) "NCT01855750 - A Randomized, Double-blind, Placebo-controlled Phase 3 Study of the Bruton's Tyrosine Kinase (BTK) Inhibitor, PCI-32765 (Ibrutinib), in Combination With Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (R-CHOP) in Subjects With Newly Diagnosed Non-Germinal Center B-Cell Subtype of Diffuse Large B-Cell Lymphoma"
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      string(187) "nct01855750-a-randomized-double-blind-placebo-controlled-phase-3-study-of-the-brutons-tyrosine-kinase-btk-inhibitor-pci-32765-ibrutinib-in-combination-with-rituximab-cyclophosphamide-doxo"
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  ["project_title"]=>
  string(113) "Predicting Overall Survival Benefit and Toxicity from Ibrutinib-R-CHOP in Non-GCB DLBCL: An IPD Post Hoc Analysis"
  ["project_narrative_summary"]=>
  string(551) "The study aims to identify baseline clinical predictors of individualized benefit and harm from adding ibrutinib, with a focus on overall survival and clinically important adverse events. Secondary outcomes include progression-free survival, response, serious adverse events, treatment discontinuation, infections, and hematologic toxicities. Treatment effect heterogeneity will be assessed using prognostic risk models, treatment-by-covariate interactions, Cox and logistic regression, restricted mean survival time analyses, and internal validation."
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(7) "Junping"
    ["last_name"]=>
    string(4) "Yang"
    ["degree"]=>
    string(6) "Doctor"
    ["primary_affiliation"]=>
    string(53) "The First Affiliated Hospital of Zhengzhou University"
    ["email"]=>
    string(26) "yangjp6@alumni.sysu.edu.cn"
    ["state_or_province"]=>
    string(5) "Henan"
    ["country"]=>
    string(5) "China"
  }
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  ["project_ext_grants"]=>
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    ["value"]=>
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    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_funding_source"]=>
  string(125) "This study was supported by the Henan Provincial Health Commission Joint Project (LHGJ20190045 [ZS] and LHGJ20190389 [YJP]). "
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(736) "Background: Diffuse large B-cell lymphoma is an aggressive blood cancer, and some patients respond better to treatment than others.

Objective: This study will examine whether baseline patient characteristics can identify who benefits or is harmed by adding ibrutinib to R-CHOP.

Study Design: Secondary analysis of de-identified data from a randomized phase III trial.

Participants: Adults with previously untreated non-GCB diffuse large B-cell lymphoma.

Primary and Secondary Outcome Measure(s): Survival, disease control, treatment response, and serious side effects.

Statistical Analysis: Survival models, subgroup analyses, and risk prediction methods will estimate individualized benefit and harm."
  ["project_brief_bg"]=>
  string(2899) "Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma. Although rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone has been a standard first-line regimen, outcomes remain heterogeneous, particularly in patients with the non--germinal center B-cell--like subtype. This biological subtype is characterized by more frequent activation of B-cell receptor and NF-kB signaling pathways, providing a rationale for targeted inhibition of Bruton tyrosine kinase with ibrutinib.



The randomized phase III trial of ibrutinib plus R-CHOP in previously untreated non-GCB DLBCL addressed an important clinical question: whether adding a targeted agent to standard immunochemotherapy improves outcomes in a biologically defined population. The study generated influential evidence regarding efficacy and safety, including signals that treatment benefit and toxicity may differ according to patient characteristics. Such heterogeneity is clinically important because a neutral or modest average treatment effect can obscure meaningful benefit in some patients and excess harm in others.



This project will use de-identified individual participant-level data from the trial to evaluate heterogeneity of treatment benefit and harm. Rather than repeating the original primary analysis, we will apply prediction and risk-modeling methods to estimate whether baseline clinical factors can identify patients more likely to experience improved overall survival from ibrutinib plus R-CHOP and patients more likely to experience serious toxicity. Outcomes will include overall survival, progression-related endpoints, response, and clinically important adverse events.



The proposed work is significant for three reasons. First, it may help refine patient selection for BTK inhibitor--based therapy in non-GCB DLBCL by moving beyond average trial-level effects toward individualized benefit-risk estimation. Second, it may improve interpretation of prior randomized evidence by clarifying whether treatment effects vary across baseline risk groups or clinically recognizable subpopulations. Third, it may inform the design of future lymphoma trials by identifying candidate predictive factors, risk strata, and endpoints that warrant prospective validation.



The public health relevance of this project lies in improving the safe and effective use of intensive lymphoma therapy. Patients with aggressive lymphoma often require urgent treatment decisions, and tools that better estimate expected benefit and toxicity could support shared decision-making and more personalized care. Because the analysis uses rigorously collected randomized trial data, the findings may materially enhance generalizable knowledge regarding treatment effect heterogeneity, precision oncology, and benefit-risk assessment in DLBCL."
  ["project_specific_aims"]=>
  string(1228) "The overall objective of this project is to evaluate whether baseline patient characteristics can identify individuals with non-GCB DLBCL who derive greater benefit or experience greater harm from adding ibrutinib to R-CHOP.



Aim 1: To evaluate heterogeneity of treatment effect for overall survival with ibrutinib plus R-CHOP versus placebo plus R-CHOP.

Hypothesis: The overall survival benefit of ibrutinib-based therapy varies according to baseline clinical risk and selected patient characteristics.



Aim 2: To develop and internally validate prognostic models for survival and toxicity outcomes using baseline variables available in the randomized trial dataset.

Hypothesis: Baseline demographic, clinical, laboratory, and disease-related factors can stratify patients into groups with different risks of death, disease progression, and serious adverse events.



Aim 3: To estimate individualized benefit-risk profiles by integrating predicted efficacy and toxicity risks.

Hypothesis: Some patients will have a favorable predicted benefit-risk profile for ibrutinib plus RCHOP, whereas others may have limited expected benefit or higher predicted toxicity risk."
  ["project_study_design"]=>
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      ["label"]=>
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  ["project_research_methods"]=>
  string(1867) "Data will be obtained from the randomized phase III trial evaluating ibrutinib plus rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone versus placebo plus R-CHOP in patients with previously untreated non--germinal center B-cell--like diffuse large B-cell lymphoma. Only de-identified individual participant-level data and supporting documentation made available through the YODA Project will be used.



The study sample will include all randomized trial participants who meet the original trial eligibility criteria and have available treatment assignment and baseline data. Key inclusion criteria for this secondary analysis are: adults with previously untreated diffuse large B-cell lymphoma classified as non-GCB according to the trial-defined method; randomization to ibrutinib plus R-CHOP or placebo plus R-CHOP; and availability of follow-up data for survival, disease response, or safety outcomes.



No additional exclusions will be applied beyond those used in the original trial, except when required for a specific analysis because of missing outcome information or absence of essential baseline covariates. Participants will not be excluded based on age, sex, performance status, disease stage, International Prognostic Index components, laboratory values, response status, treatment exposure, dose modification, or adverse event occurrence, as these factors are central to the proposed evaluation of heterogeneity of treatment benefit and harm.



No external participant-level datasets will be used or pooled with the requested YODA data. If published aggregate information is used, it will be used only for background interpretation and not combined statistically with individual participant-level data. All analyses will be performed within the secure YODA data sharing platform using R/RStudio."
  ["project_main_outcome_measure"]=>
  string(740) "The primary outcome will be overall survival, defined as time from randomization to death from any cause; living participants will be censored at last known follow-up. Secondary efficacy outcomes will include progression-free survival, event-free survival, objective response, and complete response, as defined in the original trial protocol. Secondary safety outcomes will include serious adverse events, grade 3 or higher adverse events, adverse events leading to treatment discontinuation, infections, febrile neutropenia, cytopenias, bleeding, cardiac events, and other treatment-emergent adverse events of special interest. Outcome definitions will follow the original trial whenever possible, with deviations documented transparently."
  ["project_main_predictor_indep"]=>
  string(767) "The main independent variable will be randomized treatment assignment to ibrutinib plus R-CHOP versus placebo plus R-CHOP. Efficacy analyses will follow the intention-to-treat principle; safety analyses will use the trial-defined safety population when available. Additional independent variables will include prespecified baseline demographic, clinical, laboratory, and disease-related factors, such as age, sex, performance status, stage, International Prognostic Index components or score, lactate dehydrogenase, extranodal disease, bulky disease, blood counts, organ function, and B symptoms. Treatment-effect heterogeneity will be assessed using treatment-by-covariate interactions and prognostic risk scores, with continuous variables retained when appropriate."
  ["project_other_variables_interest"]=>
  string(827) "Additional variables will describe the study population, support prognostic modeling, assess effect modification, and inform adjusted or sensitivity analyses. Demographic variables will include age, sex, race/ethnicity, geographic region, height, weight, and body mass index. Baseline clinical variables will include ECOG performance status, Ann Arbor stage, B symptoms, bulky disease, extranodal and bone marrow involvement, disease burden, and International Prognostic Index components or score. Laboratory variables will include LDH, blood counts, albumin, creatinine, liver function tests, and other chemistries. Treatment and safety variables, including dose modifications, discontinuation, subsequent therapy, adverse event type, grade, seriousness, and timing, will support descriptive, safety, and sensitivity analyses."
  ["project_stat_analysis_plan"]=>
  string(2347) "Baseline demographic, clinical, laboratory, disease-related, and treatment characteristics will be summarized overall and by randomized treatment arm. Continuous variables will be reported as means with standard deviations or medians with interquartile ranges, and categorical variables as frequencies and percentages. Baseline balance will be assessed descriptively using standardized mean differences, and missing data patterns will be summarized for candidate predictors and outcomes.



Overall survival will be analyzed according to the intention-to-treat principle. Kaplan-Meier curves will be generated by treatment arm, and Cox proportional hazards models will estimate hazard ratios and 95% confidence intervals for ibrutinib plus R-CHOP versus placebo plus R-CHOP. Restricted mean survival time differences may be estimated to provide absolute treatment-effect measures, especially if proportional hazards assumptions are not met.



Treatment-effect heterogeneity will be evaluated using prespecified baseline variables selected for clinical relevance, including age, sex, ECOG performance status, stage, International Prognostic Index, LDH, extranodal or bone marrow involvement, bulky disease, B symptoms, laboratory values, and geographic region. Cox or logistic regression models will include treatment-by-covariate interaction terms, with continuous variables modeled flexibly when appropriate. Subgroup-specific relative and absolute treatment effects will be reported cautiously.



A baseline prognostic model for overall survival will be developed using only pretreatment variables, excluding treatment assignment. Predicted-risk strata will be used to estimate treatment effects across risk groups. Separate toxicity prediction models may be developed for serious or grade 3+ adverse events and selected toxicities. Predicted survival benefit and toxicity risk will be combined descriptively to estimate individualized benefit-risk profiles.



Prediction models will be internally validated using bootstrap resampling or cross-validation, with discrimination and calibration assessed. Missing covariates may be addressed using multiple imputation. Sensitivity analyses will evaluate alternative risk strata, model specifications, safety populations, and complete-case analyses."
  ["project_software_used"]=>
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      ["label"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(1551) "The anticipated project start date is within one month of approval of the data request, completion of the Data Use Agreement, and access to the secure YODA data sharing platform.



Month 1: Complete data access setup; review the trial protocol, statistical analysis plan, case report forms, data dictionary, and supporting documentation; confirm endpoint, treatment, covariate, and safety variable definitions; assess data completeness and missing data patterns; and finalize the detailed statistical analysis plan.



Months 2--3: Conduct descriptive analyses of baseline demographic, clinical, laboratory, disease-related, treatment, and safety variables. Complete primary overall survival analyses and secondary efficacy analyses using Kaplan-Meier methods and Cox regression models. Conduct initial safety analyses.



Months 3--4: Complete heterogeneity of treatment effect analyses, including prespecified treatment by-covariate interaction models, baseline prognostic risk modeling, treatment-effect estimation across predicted-risk strata, model validation, and planned sensitivity analyses.



Months 4--5: Complete final interpretation of findings; prepare tables, figures, and supplementary materials; and draft the manuscript.



Months 5--6: Complete internal coauthor review and manuscript revisions; submit the first manuscript to a peer-reviewed oncology or hematology journal; and report study results back to the YODA Project in accordance with YODA data use requirements."
  ["project_dissemination_plan"]=>
  string(1621) "The primary product of this study will be a peer-reviewed manuscript reporting the heterogeneity of treatment effect and individualized benefit-risk analyses of ibrutinib plus R-CHOP versus placebo plus R-CHOP in patients with previously untreated non-GCB diffuse large B-cell lymphoma. The manuscript will describe the study rationale, analytic methods, baseline risk modeling approach, subgroup and interaction analyses, survival and safety outcomes, model performance, and clinical implications.



The target audience will include hematologists, oncologists, clinical trialists, lymphoma researchers, biostatisticians, and decision-makers interested in precision oncology, treatment selection, and secondary analyses of randomized clinical trial data.



Potential target journals include Blood Advances, Haematologica, Leukemia & Lymphoma, Clinical Cancer Research, The Oncologist, JCO Precision Oncology, or Cancer Medicine, depending on the final scope and strength of findings. If appropriate, findings may also be submitted as an abstract to a major hematology or oncology conference, such as the American Society of Hematology Annual Meeting, the American Society of Clinical Oncology Annual Meeting, or the International Conference on Malignant Lymphoma.



Results will be reported back to the YODA Project in accordance with data use requirements. Any dissemination will acknowledge the YODA Project and the original trial investigators as required. No attempt will be made to identify individual participants, and all results will be presented only in aggregate form."
  ["project_bibliography"]=>
  string(2113) "

Younes A, Sehn LH, Johnson P, et al. Randomized phase III trial of ibrutinib and rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone in non–germinal center B-cell diffuse large B-cell lymphoma. Journal of Clinical Oncology. 2019;37(15):1285-1295.
International Non-Hodgkin’s Lymphoma Prognostic Factors Project. A predictive model for aggressive non-Hodgkin’s lymphoma. New England Journal of Medicine. 1993;329(14):987-994.
Zhou Z, Sehn LH, Rademaker AW, et al. An enhanced International Prognostic Index for diffuse large B-cell lymphoma treated in the rituximab era. Blood. 2014;123(6):837-842.
Schmitz N, Zeynalova S, Nickelsen M, et al. CNS International Prognostic Index: a risk model for CNS relapse in patients with diffuse large B-cell lymphoma treated with R-CHOP. Journal of Clinical Oncology. 2016;34(26):3150-3156.
Coiffier B, Thieblemont C, Van Den Neste E, et al. Long-term outcome of patients in the LNH-98.5 trial, the first randomized study comparing rituximab-CHOP to standard CHOP chemotherapy in DLBCL patients. Blood. 2010;116(12):2040-2045.
Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403(6769):503-511.
Hans CP, Weisenburger DD, Greiner TC, et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood. 2004;103(1):275-282.
Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ. 2018;363:k4245.
Kent DM, Paulus JK, van Klaveren D, et al. The Predictive Approaches to Treatment effect Heterogeneity statement. Annals of Internal Medicine. 2020;172(1):35-45.
Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. 2nd ed. Cham, Switzerland: Springer; 2019.

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