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  string(1356) "Background: Abiraterone acetate and apalutamide improve outcomes in advanced prostate cancer, but treatment effects may vary across disease settings and patient characteristics.



Objective: To evaluate the efficacy and safety of abiraterone acetate and apalutamide using individual participant data (IPD) from randomized controlled trials.



Study Design: IPD meta-analysis of eligible randomized controlled trials available through the YODA Project.



Participants: Adults with prostate cancer enrolled in randomized trials comparing abiraterone acetate or apalutamide with an appropriate control treatment.



Primary and Secondary Outcome Measure(s): The primary outcome will be overall survival. Secondary outcomes will include progression-related outcomes, time-to-event measures, and treatment-related adverse events.



Statistical Analysis: Trial-level data will be harmonized and analyzed using appropriate time-to-event and binary outcome models. Treatment effects will be estimated as hazard ratios or risk ratios with 95% confidence intervals. Subgroup and interaction analyses will assess heterogeneity of treatment effects according to baseline disease characteristics and patient factors, with appropriate adjustment for trial effects and multiple comparisons where applicable."
  ["project_brief_bg"]=>
  string(2261) "Prostate cancer is a major global health burden, particularly in men with advanced or metastatic disease. Androgen receptor pathway inhibition has become an important component of treatment, and both abiraterone acetate and apalutamide have demonstrated clinical benefits in randomized controlled trials. However, evidence is distributed across trials conducted in different disease settings and patient populations, making it difficult to determine the consistency of treatment effects and which patients derive the greatest benefit.



This project will use individual participant data (IPD) from eligible randomized trials available through the YODA Project. Compared with conventional aggregate-data meta-analysis, IPD analysis allows standardized definitions of outcomes and baseline characteristics across trials and enables more detailed evaluation of treatment-effect heterogeneity. The project will synthesize evidence on survival, disease progression, and safety while examining whether treatment effects vary according to clinically relevant patient and disease characteristics.



The significance of this work is to generate more precise and generalizable evidence regarding the effectiveness and safety of abiraterone acetate and apalutamide in prostate cancer. The findings may help clarify the consistency of treatment benefits across clinical settings, identify patient groups with greater or lesser expected benefit, and support evidence-based treatment selection and future clinical research. The results will be reported in accordance with established principles for IPD meta-analysis and will be used to enhance generalizable scientific and medical knowledge rather than to make decisions about individual patients.



Representative prior studies include: James ND, et al. N Engl J Med. 2017;377:338-351 (abiraterone in newly diagnosed metastatic prostate cancer); Fizazi K, et al. N Engl J Med. 2017;377:352-360 (abiraterone in metastatic castration-sensitive prostate cancer); Chi KN, et al. N Engl J Med. 2019;381:13-24 (apalutamide in nonmetastatic castration-resistant prostate cancer); Chi KN, et al. N Engl J Med. 2021;384:216-228 (apalutamide in metastatic castration-sensitive prostate cancer)."
  ["project_specific_aims"]=>
  string(997) "Aim 1: To quantify the effects of abiraterone acetate and apalutamide on overall survival and other clinically relevant efficacy outcomes in patients with prostate cancer using IPD from randomized controlled trials.



Aim 2: To characterize the safety profiles of abiraterone acetate and apalutamide, including treatment-related adverse events and clinically important toxicities.



Aim 3: To investigate heterogeneity of treatment effects according to baseline patient characteristics and disease status and identify subgroups with potentially greater or smaller treatment benefit.



We hypothesize that both treatments improve clinically relevant outcomes compared with control therapy, but that the magnitude of benefit varies across disease settings and patient subgroups. We further hypothesize that the IPD approach will identify clinically meaningful differences in treatment effects that may not be apparent from conventional aggregate-data analyses."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
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  ["project_purposes"]=>
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    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]=>
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      ["value"]=>
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      ["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(69) "confirm_or_validate previously_conducted_research_on_treatment_safety"
      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
    [4]=>
    array(2) {
      ["value"]=>
      string(59) "preliminary_research_to_be_used_as_part_of_a_grant_proposal"
      ["label"]=>
      string(59) "Preliminary research to be used as part of a grant proposal"
    }
    [5]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [6]=>
    array(2) {
      ["value"]=>
      string(37) "participant_level_data_only_from_yoda"
      ["label"]=>
      string(51) "Meta-analysis using only data from the YODA Project"
    }
    [7]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [8]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
    [9]=>
    array(2) {
      ["value"]=>
      string(28) "research_on_comparison_group"
      ["label"]=>
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  ["project_research_methods"]=>
  string(1623) "Data will be obtained from randomized controlled trials of abiraterone acetate and apalutamide made available through the YODA Project. For each trial, the study population will be defined according to the eligibility criteria of the original trial. We will include adult participants with prostate cancer who were randomized to an abiraterone acetate-containing regimen, an apalutamide-containing regimen, or the corresponding control regimen and for whom treatment assignment and relevant outcome data are available.



The original trial eligibility criteria regarding age, disease stage, castration sensitivity/resistance status, prior treatment, performance status, and other clinical characteristics will be retained for each individual trial. Participants who were not randomized, did not meet the original trial eligibility criteria, or have no evaluable treatment assignment will be excluded. Additional exclusions will be limited to participants with missing data that are essential for a specific analysis.



No additional disease- or treatment-specific exclusion criteria will be imposed across trials, in order to preserve the randomized study populations and minimize selection bias. Trial-specific inclusion and exclusion criteria will be documented separately and considered when interpreting pooled analyses.



No data from studies outside the YODA Project will be used for the primary IPD analyses. All individual participant data analyses will be conducted within the data environment and according to the data-use requirements specified by the YODA Project.

"
  ["project_main_outcome_measure"]=>
  string(1629) "Primary Outcome:



Overall survival (OS), defined as the time from randomization to death from any cause. Participants alive at the date of last available follow-up will be censored at that date.



Secondary Outcomes:



Progression-free survival (PFS) or the corresponding progression-related endpoint reported in each trial, defined according to the original trial protocol, preferably as time from randomization to disease progression or death from any cause.

Time to subsequent disease progression or other clinically relevant time-to-event outcomes, when consistently available across trials.

Prostate cancer-specific or disease-related outcomes, including radiographic progression-free survival (rPFS), when available and sufficiently comparable across trials.

Treatment-related adverse events, including grade ≥3 adverse events, serious adverse events, treatment discontinuation due to adverse events, and clinically important treatment-specific toxicities.

Other efficacy outcomes reported consistently across eligible trials may be analyzed as exploratory secondary outcomes.



For pooled analyses, outcome definitions will be harmonized across trials where possible. When endpoint definitions differ between trials, the original trial definitions will be retained and differences will be considered in sensitivity analyses. No changes to the prespecified primary outcome are planned after access to the data. Any additional exploratory outcomes identified after data access will be clearly labeled as exploratory in the final publication."
  ["project_main_predictor_indep"]=>
  string(1037) "The primary independent variable will be randomized treatment assignment. Treatment will be categorized into three groups: (1) abiraterone acetate-containing treatment, (2) apalutamide-containing treatment, and (3) the corresponding control treatment.



For each trial, the treatment variable will initially be coded according to the original randomized treatment arms. For pooled analyses, treatment categories will be harmonized across trials while preserving the distinction between abiraterone acetate and apalutamide.



The primary comparisons will evaluate the effect of abiraterone acetate versus control and apalutamide versus control. Where methodologically appropriate, an indirect comparison between abiraterone acetate and apalutamide will be explored using the randomized trial evidence and appropriate statistical methods. Randomized treatment assignment, rather than treatment actually received, will be used as the primary exposure definition in accordance with the intention-to-treat principle."
  ["project_other_variables_interest"]=>
  string(1596) "Other variables of interest will include baseline demographic, clinical, disease, and treatment characteristics available in the requested datasets.



Demographic variables will include age at randomization and other available demographic characteristics. Clinical variables will include performance status, relevant comorbidities, baseline laboratory measurements, and prior or concomitant treatments when available.



Disease-related variables will include disease stage, metastatic status, castration-sensitive or castration-resistant disease status, Gleason score or Grade Group, PSA level, tumor burden, presence and location of metastases, and other prognostic characteristics available in the individual trials.



Treatment-related variables will include prior prostate cancer therapies, prior androgen-deprivation therapy, duration of prior treatment, subsequent therapies, treatment duration, treatment discontinuation, and dose modifications where available.



Trial-level variables will include trial identifier, recruitment period, treatment arm, control regimen, and relevant trial-specific endpoint definitions.



Baseline variables will primarily be used to describe the study population and investigate heterogeneity of treatment effects. Variables available consistently across trials will be considered for multivariable adjustment and interaction analyses. Missing data will be characterized for each variable and handled using prespecified methods appropriate to the extent and mechanism of missingness.

"
  ["project_stat_analysis_plan"]=>
  string(3433) "Baseline characteristics will be summarized by randomized treatment group and trial using means and standard deviations or medians and interquartile ranges for continuous variables, and counts and percentages for categorical variables. Balance between randomized treatment groups will be described without relying solely on statistical significance tests.



The primary analysis will follow the intention-to-treat principle. Overall survival and other time-to-event outcomes will be analyzed using Kaplan–Meier methods. Median survival and survival probabilities at clinically relevant time points will be reported when estimable. Treatment effects will primarily be expressed as hazard ratios (HRs) with 95% confidence intervals using Cox proportional hazards models.



Trial-specific treatment effects will first be estimated separately. Pooled analyses will then be performed using stratified or multivariable Cox models accounting for trial effects, as appropriate. Proportional hazards assumptions will be assessed. If substantial departures from proportional hazards are identified, alternative approaches such as restricted mean survival time or time-varying treatment effects will be considered.



For binary safety outcomes, risks and risk ratios or odds ratios with 95% confidence intervals will be estimated as appropriate. Adverse events will be summarized by treatment group, severity, seriousness, and treatment discontinuation where available.



Heterogeneity of treatment effects will be evaluated using treatment-by-subgroup interaction terms. Prespecified subgroup analyses will include clinically relevant baseline characteristics such as age, disease status, metastatic status, castration sensitivity/resistance status, baseline PSA, disease burden, performance status, and prior treatment, subject to availability and comparability across trials. Interaction effects will be emphasized rather than interpreting subgroup-specific statistical significance in isolation.



For the comparison of abiraterone acetate and apalutamide, evidence from trials sharing an appropriate common comparator will be synthesized using methods suitable for indirect treatment comparison. Where assumptions of transitivity and comparability are considered reasonable, a network meta-analytic or equivalent indirect-comparison framework may be used. The distinction between direct randomized comparisons and indirect evidence will be maintained.



Missing baseline covariate data will be described. When appropriate, multiple imputation or other validated methods will be used for covariate adjustment and subgroup analyses. For primary time-to-event analyses, censoring will follow the definitions of the original trials.



Sensitivity analyses will assess the robustness of findings according to trial characteristics, endpoint definitions, missing-data handling, and statistical model specification. Additional analyses may examine treatment effects within individual disease settings and clinically relevant subgroups.



All analyses will be conducted using the individual participant data available through the YODA Project and will comply with the applicable data-use and analysis requirements. Any analyses that are not prespecified in this application will be identified as exploratory in the final publication.

"
  ["project_software_used"]=>
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    array(2) {
      ["value"]=>
      string(6) "python"
      ["label"]=>
      string(6) "Python"
    }
    [1]=>
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      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
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  ["project_timeline"]=>
  string(1351) "Anticipated project start: October 2026



October–November 2026: Data receipt, data dictionary review, data cleaning, harmonization of variables and outcome definitions, and development of the final analysis dataset.



December 2026–February 2027: Primary statistical analyses, including treatment-effect estimation, survival analyses, safety analyses, and assessment of heterogeneity across clinically relevant subgroups.



March 2027: Completion of sensitivity analyses, interpretation of results, and finalization of statistical outputs.



April 2027: Completion of the first manuscript draft.



May 2027: Internal review, revision, and preparation of the manuscript and supplementary materials.



June 2027: First submission of the manuscript to a peer-reviewed journal.



July–September 2027: Response to peer review and additional analyses or revisions if required.



September 2027: Final reporting of study results and research outputs to the YODA Project.



The project is planned to be completed within the 12-month data access period. If additional time is required for manuscript revision or publication-related analyses, an extension will be requested in accordance with the YODA Project Data Use Agreement.

"
  ["project_dissemination_plan"]=>
  string(1599) "The primary product of this project will be a peer-reviewed scientific manuscript reporting the individual participant data meta-analysis of randomized controlled trials evaluating abiraterone acetate and apalutamide in prostate cancer. The manuscript will report treatment efficacy, safety, and clinically relevant heterogeneity of treatment effects across patient and disease subgroups.



Results will be disseminated to the clinical and research communities through submission to an international peer-reviewed oncology or urology journal and, where appropriate, presentation at scientific conferences. Potential target journals include European Urology, European Urology Oncology, Journal of Clinical Oncology, JAMA Oncology, or other appropriate journals based on the final scope and strength of the findings.



The intended audience includes medical oncologists, urologists, radiation oncologists, clinical researchers, guideline developers, and researchers in evidence-based medicine and meta-analysis. Findings may also be relevant to healthcare policymakers and organizations involved in prostate cancer treatment recommendations.



The study results will be reported to the YODA Project in accordance with the applicable Data Use Agreement. Publications will clearly describe the use of YODA-provided individual participant data and will follow applicable requirements for data citation, acknowledgment, reporting, and responsible dissemination. Any additional exploratory analyses will be clearly identified as such in the final publication.

"
  ["project_bibliography"]=>
  string(784) "
1. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010 Feb 5;340:c221.

2. Scher HI, Morris MJ, Stadler WM, Higano C, Basch E, Fizazi K, Antonarakis ES, Beer TM, Carducci MA, Chi KN, Corn PG, de Bono JS, Dreicer R, George DJ, Heath EI, Hussain M, Kelly WK, Liu G, Logothetis C, Nanus D, Stein MN, Rathkopf DE, Slovin SF, Ryan CJ, Sartor O, Small EJ, Smith MR, Sternberg CN, Taplin ME, Wilding G, Nelson PS, Schwartz LH, Halabi S, Kantoff PW, Armstrong AJ; Prostate Cancer Clinical Trials Working Group 3. Trial Design and Objectives for Castration-Resistant Prostate Cancer: Updated Recommendations From the Prostate Cancer Clinical Trials Working Group 3. J Clin Oncol. 2016 Apr 20;34(12):1402-18.
"
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products
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