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      string(274) "NCT01867710 - A Randomized Phase 2 Study Evaluating Abiraterone Acetate With Different Steroid Regimens for Preventing Symptoms Associated With Mineralocorticoid Excess in Asymptomatic, Chemotherapy-naïve and Metastatic Castration-resistant Prostate Cancer (mCRPC) Patients"
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  ["project_title"]=>
  string(87) "Dynamic Survival and Safety Prediction in Chemotherapy-Naïve mCRPC: A Pooled IPD Study"
  ["project_narrative_summary"]=>
  string(833) "Men with chemotherapy-naïve metastatic castration-resistant prostate cancer (mCRPC) are followed during treatment with prostate-specific antigen (PSA), routine blood tests, symptoms, treatment records, adverse events (AEs), radiographic progression status, and tumor measurements when available. Many prediction models use only information known before treatment starts. This study will use de-identified participant-level data from four randomized abiraterone-based mCRPC trials to test whether early changes during treatment improve prediction of later overall survival (OS), radiographic progression-free survival (rPFS), and clinically relevant AEs. The study will not re-estimate treatment effects or recommend treatment changes. It aims to show how routine trial follow-up data can support better prognostic updating in mCRPC."
  ["project_learn_source"]=>
  string(12) "scien_public"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Wenxi"
    ["last_name"]=>
    string(4) "Tang"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(31) "China Pharmaceutical University"
    ["email"]=>
    string(18) "tokammy@cpu.edu.cn"
    ["state_or_province"]=>
    string(7) "Jiangsu"
    ["country"]=>
    string(5) "China"
  }
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      ["p_pers_f_name"]=>
      string(7) "Hanqiao"
      ["p_pers_l_name"]=>
      string(4) "Shao"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(31) "China Pharmaceutical University"
      ["p_pers_scop_id"]=>
      string(11) "58644324500"
      ["requires_data_access"]=>
      string(3) "yes"
    }
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    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Mingye"
      ["p_pers_l_name"]=>
      string(4) "Zhao"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(31) "China Pharmaceutical University"
      ["p_pers_scop_id"]=>
      string(11) "57429571800"
      ["requires_data_access"]=>
      string(3) "yes"
    }
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  ["project_ext_grants"]=>
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    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
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  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1465) "Background: In chemotherapy-naïve mCRPC, prognosis is often estimated from baseline factors, although early follow-up data may also be informative.

Objective: To develop and validate dynamic prognostic models for OS, rPFS, and clinically relevant AE outcomes using early prostate-specific antigen (PSA), laboratory, symptom, treatment-exposure, early adverse-event history, and tumor-measurement data.

Study Design: Retrospective pooled secondary analysis of de-identified participant-level data from COU-AA-302, ABI-PRO-3002, ACIS, and NCT01867710, using landmark prediction and internal-external validation.

Participants: Randomized patients with chemotherapy-naïve mCRPC.

Primary and Secondary Outcome Measure(s): Primary outcome: post-landmark OS. Secondary outcomes: rPFS, grade ≥3 AEs, serious AEs, selected clinically relevant AEs, and AE-related treatment discontinuation.

Statistical Analysis: Trial-stratified Cox models will be used for OS and rPFS, comparing baseline-only models, baseline plus PSA models, baseline plus PSA and laboratory models, and multi-domain models. Sum of longest diameters (SLD)-based tumor growth inhibition (TGI) analyses will be exploratory in measurable soft-tissue disease. AE outcomes will be analyzed using landmark logistic regression, cause-specific Cox models, or discrete-time person-period models, with predictors restricted to information available before the prediction time."
  ["project_brief_bg"]=>
  string(3217) "Metastatic castration-resistant prostate cancer (mCRPC) has variable clinical trajectories, even among chemotherapy-naïve patients. Established prognostic models rely mainly on baseline factors such as performance status, metastatic distribution, PSA, alkaline phosphatase, lactate dehydrogenase, hemoglobin, albumin, pain, and prior therapy, but they do not fully capture how risk is reassessed after treatment begins. During follow-up, clinicians repeatedly observe PSA, laboratory values, symptoms, treatment exposure, adverse events (AEs), pre-landmark radiographic assessment information, and recorded tumor measurements where available. PSA kinetics have been associated with overall survival in abiraterone-treated castration-resistant prostate cancer, and nonlinear PSA-survival joint modelling has been proposed to characterise the relationship between longitudinal PSA changes and survival.1,2 Tumor regression and growth-rate parameters have also been explored as indicators of therapeutic activity in prostate cancer.3 These early longitudinal data may improve prognostic updating beyond baseline risk.

Abiraterone acetate plus prednisone has been evaluated in randomized trials in chemotherapy-naïve mCRPC, including COU-AA-302 and ABI-PRO-3002.4,5 ACIS provides a related combination-treatment setting in which both arms received abiraterone acetate plus prednisone, with or without apalutamide.6 NCT01867710 evaluated different glucocorticoid regimens with abiraterone and is relevant for exploratory safety prediction of abiraterone- and glucocorticoid-related AEs.7 These trials represent related but distinct abiraterone-based settings, allowing assessment of whether dynamic prediction models are robust across trial contexts rather than specific to a single study.

Previous work has examined PSA kinetics and PSA-survival modelling in abiraterone-treated or prostate cancer populations.1,2 The present project asks a different question: whether early PSA, laboratory, symptom, safety, treatment-exposure, and recorded tumor-measurement data can be combined to update prediction of OS, rPFS, and clinically relevant AE outcomes across several abiraterone-based trials. The focus is prognostic modelling, validation, and cross-trial transportability, not re-estimation of treatment effects, dose comparison, imaging-frequency optimization, or development of a commercial decision tool.

The expected contribution is practical and methodological. First, the study will assess whether early PSA and laboratory biomarkers improve OS and rPFS prediction beyond baseline factors. Second, it will test whether symptoms, treatment exposure, and early AEs add value beyond PSA-based updating alone. Third, it will explore whether recorded tumor measurements, including SLD, can support tumor growth inhibition analyses in measurable soft-tissue disease.3 Fourth, internal-external validation will assess whether models developed in one trial setting transport to another. The results should clarify which routine variables and modelling strategies are useful for dynamic prognostic modelling in chemotherapy-naïve mCRPC, consistent with current prediction-model reporting principles.8"
  ["project_specific_aims"]=>
  string(1528) "Overall objective: To determine whether early on-treatment PSA, laboratory biomarkers, symptoms, treatment exposure, adverse events, and recorded tumor measurements can improve dynamic prediction of outcomes in chemotherapy-naïve mCRPC patients enrolled in four abiraterone-based randomized trials.

Aim 1: Develop and validate landmark dynamic prognostic models for overall survival and radiographic progression-free survival. Hypothesis: early PSA and laboratory biomarker changes will improve discrimination, calibration, and prediction error compared with baseline-only models.

Aim 2: Evaluate whether multi-domain dynamic information, including symptoms, treatment exposure, early adverse events, and recorded tumor measurements where available, adds prognostic value beyond PSA-based updating alone. Hypothesis: multi-domain models will improve risk stratification and clinical net benefit.

Aim 3: Predict clinically relevant adverse-event outcomes, including grade ≥3 adverse events, serious adverse events, selected adverse events of interest, and adverse-event-related discontinuation. Regression-based models and penalized regression will be used as primary approaches; selected machine-learning models will be used only as exploratory benchmarks if event counts are sufficient.

Aim 4: Explore tumor growth inhibition analyses using SLD in participants with measurable soft-tissue disease, and assess model transportability across trials using internal-external validation where feasible."
  ["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(49) "new_research_question_to_examine_treatment_safety"
      ["label"]=>
      string(49) "New research question to examine treatment safety"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(37) "participant_level_data_only_from_yoda"
      ["label"]=>
      string(51) "Meta-analysis using only data from the YODA Project"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
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  }
  ["project_research_methods"]=>
  string(1966) "Requested YODA data sources are four randomized abiraterone-based trials in chemotherapy-naïve metastatic castration-resistant prostate cancer (mCRPC): COU-AA-302 (NCT00887198), ABI-PRO-3002 (NCT01591122), ACIS (NCT02257736), and NCT01867710.

Inclusion criteria for the primary study sample are: randomized participants in any of the four trials; chemotherapy-naïve mCRPC as defined by the original trial eligibility criteria; available baseline demographic/clinical data; and follow-up information sufficient to define at least one outcome of interest. For each landmark analysis, participants must be under follow-up and free of the relevant outcome before the landmark. For OS models, participants must not have died before the landmark; for rPFS models, participants must not have had radiographic progression or death before the landmark; for first-event adverse-event models, participants must not have had the specific adverse-event outcome before the landmark.

Exclusion criteria for primary analyses are: no post-randomization follow-up; missing or inconsistent key dates needed to define landmark time, outcome time, or censoring; occurrence of the relevant outcome before the landmark; or insufficient information to define the relevant endpoint. Participants will not be excluded solely because of missing predictor values if missingness can be handled using prespecified imputation or missing-data methods.

Analyses using recorded tumour measurements, including SLD and tumour growth inhibition analyses, will be restricted to participants with measurable soft-tissue disease and adequate serial tumour-measurement records. Participants without measurable soft-tissue disease will remain eligible for the main PSA, laboratory, survival, progression, and adverse-event analyses.

No non-YODA external individual participant data will be used. The pooled IPD analysis will be conducted within the YODA secure data environment."
  ["project_main_outcome_measure"]=>
  string(1707) "Primary outcome: OS, defined as time from each prespecified landmark to death from any cause. Participants alive at last available follow-up will be censored at that date.

Key secondary outcome: rPFS, defined within each trial according to the original protocol and analysis definitions as time from each landmark to radiographic progression or death, whichever occurs first. Because assessment schedules and progression definitions may vary across trials, analyses will use trial-specific endpoint definitions and trial-stratified modeling.

Safety secondary outcomes: subsequent grade 3 or higher treatment-emergent AEs after each landmark; serious AEs after each landmark; AE-related treatment discontinuation; and selected clinically relevant AEs, subject to data availability and event counts. Candidate events include hypertension, hypokalemia, hepatic abnormalities, fluid retention or edema, cardiac events, fracture, anemia, fatigue, and other clinically relevant events identified after review of trial documentation and coding dictionaries. For safety prediction, adverse events occurring before the landmark or prediction cycle will be used only as predictors, whereas outcomes will be defined as new or subsequent adverse-event outcomes occurring after that prediction time. For first-event analyses, participants with the relevant adverse-event outcome before the prediction time will be excluded from the risk set.

If event counts are insufficient for a specific AE, it will be analyzed descriptively or combined into a clinically justified composite endpoint. Any changes to endpoint definitions after review of trial documentation will be documented in the final report."
  ["project_main_predictor_indep"]=>
  string(1918) "There is no single main predictor variable for this study. The main independent variables are prespecified baseline and early on-treatment predictor domains for dynamic prediction of overall survival, radiographic progression-free survival, and clinically relevant adverse-event outcomes.

Baseline predictors will include established prognostic factors in mCRPC: age, ECOG performance status, pain or symptom status, metastatic sites, baseline PSA, alkaline phosphatase, lactate dehydrogenase, hemoglobin, albumin, liver and renal function, potassium, prior local therapy, time since diagnosis or androgen deprivation therapy, and randomized treatment arm.

Early on-treatment predictors will be assessed at prespecified landmarks, primarily weeks 8 and 12, with week 24 considered as a sensitivity landmark depending on visit schedules and data availability. These predictors will include PSA level, absolute and percentage PSA change from baseline, PSA decline or response categories based on values observed before the landmark, and changes in routinely collected laboratory biomarkers.

Additional dynamic predictors will include pain or symptom measures, treatment exposure before the landmark or prediction cycle, dose interruption or modification, and prior adverse events categorized by grade, seriousness, and clinical type. For cycle-level safety prediction, baseline characteristics and information available up to cycle t-1 will be used to predict adverse events occurring during cycle t.

Recorded tumor measurements, including SLD where available, will be used only in exploratory tumor growth inhibition analyses among participants with measurable soft-tissue disease and adequate serial records.

Analyses will emphasize prespecified predictor domains, model performance, calibration, and validation rather than isolated statistical significance of individual predictors."
  ["project_other_variables_interest"]=>
  string(1289) "Other variables will be used to describe the study population, define risk sets, harmonize data across trials, and support adjustment, sensitivity analyses, and recalibration.

These variables may include trial identifier, randomized treatment arm, abiraterone-based regimen, glucocorticoid regimen, apalutamide exposure in ACIS, treatment start and stop dates, dose interruptions or modifications, treatment duration, concomitant medications relevant to adverse-event assessment, dates of PSA and laboratory assessments, symptom or pain assessment dates, radiographic assessment dates, adverse-event onset and resolution dates, treatment discontinuation dates, radiographic progression dates, death dates, and censoring dates.

Baseline descriptive variables will include demographic characteristics, geographic region, body weight or body mass index, ECOG performance status, baseline pain status, prior local therapy, time since diagnosis, metastatic sites, measurable soft-tissue disease status, baseline disease progression type, Gleason score if available, and baseline laboratory values.

Final operational definitions will follow each trial’s protocol, statistical analysis plan, case report forms, data specification where available, and clinical study report."
  ["project_stat_analysis_plan"]=>
  string(4829) "This is a prognostic prediction study. Treatment arm and trial will be used for adjustment, stratification, sensitivity analysis, validation, or recalibration, rather than for estimating new comparative treatment effects.

Data preparation: Protocols, statistical analysis plans, case report forms, data dictionaries, and clinical study reports will be used to harmonise variables across COU-AA-302, ABI-PRO-3002, ACIS, and NCT01867710. Laboratory units, assessment dates, adverse-event coding, endpoint definitions, treatment exposure, and censoring rules will be standardised where possible. Adverse events will be mapped using available MedDRA preferred terms, system organ class, grade, seriousness, onset/resolution dates, and action taken. Variable availability, missingness, and assessment schedules will be summarised by trial and arm.

Descriptive analyses: Baseline characteristics, follow-up, event counts, treatment duration, dose interruption or modification, PSA and laboratory assessment frequency, recorded tumour-measurement availability, and adverse-event frequencies will be summarised overall and by trial. Kaplan-Meier curves will describe OS and rPFS.

Landmark strategy: The primary prediction landmarks will be weeks 8 and 12. Participants must be under follow-up and free of the relevant outcome at the landmark. Week 4 will be used only for exploratory laboratory/safety prediction, and week 24 will be used as a sensitivity landmark if data permit. Prediction horizons will include 3, 6, and 12 months after each landmark when event counts are sufficient.

Survival and rPFS modelling: For OS and rPFS, sequential landmark models will be compared: baseline-only, PSA-only, PSA plus laboratory biomarkers, and multi-domain models incorporating PSA, laboratory biomarkers, symptoms/pain, treatment exposure, early adverse events, and recorded tumour measurements where available. Cox models with trial-stratified baseline hazards will be primary. Flexible parametric survival models will be considered as sensitivity analyses. Continuous predictors may be modelled using restricted cubic splines, and penalised regression will be used if candidate predictor dimensionality is high relative to events. Proportional hazards assumptions will be assessed using Schoenfeld residuals.

Longitudinal trajectory analyses: To complement fixed-landmark models, exploratory longitudinal models will use biomarker or tumour-measurement history observed up to the prediction time. For PSA, kinetic features such as current value, slope, percentage change, time to nadir, rebound, or cumulative exposure will be evaluated, and joint PSA-OS models will be fitted if data density and convergence are adequate. For participants with measurable soft-tissue disease and adequate serial records, SLD-based tumour growth inhibition analyses will estimate shrinkage and regrowth features and test their incremental value for OS and rPFS beyond baseline, PSA, and laboratory models. SLD/TGI features will not be imputed for participants without measurable disease.

Safety prediction: Safety analyses will be conducted in the overall randomized population for general adverse-event outcomes and, where clinically appropriate, in abiraterone-exposed participants for abiraterone-relevant adverse events of special interest. Outcomes may include grade >=3 adverse events, serious adverse events, adverse-event-related discontinuation, and selected events such as hypertension, hypokalemia, hepatic abnormalities, fluid retention/edema, cardiac events, fracture, and anemia, subject to event counts. Landmark analyses will use baseline characteristics and information available before each landmark to predict subsequent adverse events after that landmark. Cycle-level analyses may use discrete-time person-period models, in which each participant contributes one record per cycle and information available up to cycle t-1, including laboratory values, PSA, symptoms, treatment exposure, dose interruption or modification, and prior adverse-event history, is used to predict adverse events during cycle t. Landmark logistic regression, cause-specific Cox models, or discrete-time hazard models will be used depending on outcome timing. Penalized regression will be used when candidate predictor dimensionality is high relative to event counts. Random forests or gradient boosting will be used only as exploratory benchmarks if event counts are sufficient, with calibration and interpretability prioritized.

Missing data and reporting: Missing predictor data will be handled using multiple imputation when assumptions are reasonable; complete-case analyses will be sensitivity analyses. Minimum sample size and event counts will be assessed using Riley criteria for prediction models. "
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      ["label"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(1242) "Months 0-1: Complete YODA Project access procedures; obtain protocols, statistical analysis plans, case report forms, clinical study reports, and datasets; confirm variable availability; finalize the analysis plan.

Months 2-3: Harmonize datasets across trials; standardize time origins, laboratory units, endpoint definitions, AE coding, and landmark datasets; calculate observed event counts and final sample size requirements.

Months 4-5: Conduct descriptive analyses; develop baseline-only and baseline plus PSA OS/rPFS models; implement the internal-external cross-validation pipeline.

Months 6-7: Develop PSA plus laboratory dynamic models; perform measurable-disease SLD/TGI subgroup analyses.

Months 8-9: Develop exploratory adverse event of special interest (AESI) safety prediction models; complete calibration, decision-curve, and sensitivity analyses.

Month 10: Conduct focused PSA-OS joint model if feasible; finalize model comparisons and validation results.

Month 11: Draft the manuscript and complete TRIPOD+AI reporting checklist.

Month 12: Submit the manuscript to a peer-reviewed journal and report results back to the YODA Project according to data-use requirements.

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  string(972) "The main product will be a peer-reviewed manuscript describing dynamic biomarker-based prognostic modeling for OS, rPFS, and clinically important abiraterone-relevant AESI in mCRPC. A secondary methodological manuscript may be prepared only if the comparison of landmark, two-stage TGI, and joint PSA-OS approaches yields findings of broader methodological interest. Target audiences include oncology clinicians, urologists, trialists, biostatisticians, prognostic model researchers, and health-services researchers interested in using trial IPD more efficiently.

Potential journals include European Urology Oncology, Clinical Genitourinary Cancer, JCO Clinical Cancer Informatics, BMC Medicine, BMC Cancer, Diagnostic and Prognostic Research, and Statistics in Medicine. Results may also be presented at ASCO, ESMO, ASCPT, or ISPOR. Any shared code, model documentation, or summary outputs will follow YODA requirements and will not contain participant-level data."
  ["project_bibliography"]=>
  string(2304) "
1. Xu XS, Ryan CJ, Stuyckens K, et al. Correlation between Prostate-Specific Antigen Kinetics and Overall Survival in Abiraterone Acetate-Treated Castration-Resistant Prostate Cancer Patients. Clin Cancer Res. 2015;21(14):3170-3177. doi:10.1158/1078-0432.CCR-14-1549

2. Desmée S, Mentré F, Veyrat-Follet C, Sébastien B, Guedj J. Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients. Biometrics. 2017;73(1):305-312. doi:10.1111/biom.12537

3. Stein WD, Gulley JL, Schlom J, et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin Cancer Res. 2011;17(4):907-917. doi:10.1158/1078-0432.CCR-10-1762

4. Ryan CJ, Smith MR, de Bono JS, et al. Abiraterone in metastatic prostate cancer without previous chemotherapy. N Engl J Med. 2013;368(2):138-148. doi:10.1056/NEJMoa1209096.

5. Ye D, Huang Y, Zhou F, Xie K, Matveev V, Li C, Alexeev B, Tian Y, Qiu M, Li H, Zhou T, De Porre P, Yu M, Naini V, Liang H, Wu Z, Sun Y. A phase 3, double-blind, randomized placebo-controlled efficacy and safety study of abiraterone acetate in chemotherapy-naïve patients with mCRPC in China, Malaysia, Thailand and Russia. Asian J Urol. 2017 Apr;4(2):75-85. doi: 10.1016/j.ajur.2017.01.002

6. Saad F, Efstathiou E, Attard G, et al. Apalutamide plus abiraterone acetate and prednisone versus placebo plus abiraterone and prednisone in metastatic, castration-resistant prostate cancer (ACIS): a randomised, placebo-controlled, double-blind, multinational, phase 3 study. Lancet Oncol. 2021;22(11):1541-1559. doi:10.1016/S1470-2045(21)00402-2.

7. Attard G, Merseburger AS, Arlt W, et al. Assessment of the Safety of Glucocorticoid Regimens in Combination With Abiraterone Acetate for Metastatic Castration-Resistant Prostate Cancer: A Randomized, Open-label Phase 2 Study. JAMA Oncol. 2019;5(8):1159-1167. doi:10.1001/jamaoncol.2019.1011.

8. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. doi:10.1136/bmj-2023-078378.
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