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  ["project_title"]=>
  string(110) "Understanding Heterogeneity and Improving the Precision of Prostate Cancer Clinical Outcomes in the Modern Era"
  ["project_narrative_summary"]=>
  string(780) "Prostate cancer is one of the most common cancers in men. Some men develop metastatic hormone-sensitive prostate cancer (mHSPC), where the cancer has spread but still responds to hormone therapy. This study will use data from more than 10,000 participants in completed clinical trials to improve treatment decisions. Researchers will determine whether early signs of disease worsening, such as changes on scans or starting a new treatment, can predict survival. They will also develop prediction models using clinical information to identify patients at different levels of risk and evaluate how the order of treatments affects survival and quality of life. The findings may help speed clinical trials and support more personalized treatment for men with advanced prostate cancer."
  ["project_learn_source"]=>
  string(5) "other"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Susan"
    ["last_name"]=>
    string(6) "Halabi"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(15) "Duke University"
    ["email"]=>
    string(21) "susan.halabi@duke.edu"
    ["state_or_province"]=>
    string(2) "NC"
    ["country"]=>
    string(13) "United States"
  }
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    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_funding_source"]=>
  string(3) "DOD"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1620) "Background: mHSPC is a heterogeneous disease with substantial variation in treatment response and survival. Although several effective therapies are available, reliable early endpoints, prognostic models, and evidence to guide treatment sequencing remain limited.

Objective: To validate intermediate clinical endpoints as surrogate measures for overall survival (OS), develop and validate prognostic models for OS, characterize treatment sequencing patterns, and evaluate their associations with survival and quality of life (QOL) in men with mHSPC.

Study Design: Individual participant data meta-analysis using harmonized data from multiple completed randomized clinical trials.

Participants: More than 10,000 men with mHSPC enrolled in international phase III randomized clinical trials.

Primary and Secondary Outcome Measure(s): The primary outcome is OS. Secondary outcomes include radiographic progression-free survival, clinical progression-free survival, treatment duration, treatment sequencing, and patient-reported QOL.

Statistical Analysis: Surrogacy between intermediate endpoints and OS will be evaluated using established individual- and trial-level validation methods. Multivariable regression and machine learning approaches will be used to develop and externally validate prognostic models incorporating demographic, clinical, laboratory, treatment, and longitudinal patient-reported data. Treatment patterns and sequencing will be characterized, and causal inference methods will estimate their associations with OS and QOL while accounting for baseline confounding. "
  ["project_brief_bg"]=>
  string(3149) "MHSPC is a major cause of cancer-related morbidity and mortality worldwide. Although multiple therapies, including androgen receptor pathway inhibitors, chemotherapy, and combination regimens, have substantially improved survival, considerable heterogeneity exists in treatment response and long-term outcomes. Clinicians currently lack robust tools to identify which patients are most likely to benefit from specific treatment strategies and the optimal sequence of therapies. In addition, overall survival (OS), the gold-standard endpoint in oncology clinical trials, often requires many years of follow-up, delaying the evaluation and approval of promising new therapies.(Davis et al., 2019; Fizazi et al., 2022; Attard et al., 2023)

This project will leverage harmonized individual participant data (IPD) from more than 10,000 men enrolled in multiple completed phase III randomized clinical trials of mHSPC to address several important clinical and methodological questions. First, we will evaluate whether intermediate clinical endpoints, including radiographic progression-free survival (rPFS) and clinical progression-free survival (cPFS), can serve as valid surrogate endpoints for OS. Previous work from the ICECaP Working Group and our recent analyses identified these endpoints as promising surrogates, but validation in contemporary patients receiving modern treatment intensification remains necessary.(Sweeney et al., 2015; Halabi et al., 2024) Reliable surrogate endpoints would substantially shorten future clinical trials, reduce development costs, and accelerate the availability of effective treatments for patients.

Second, we will develop and externally validate prognostic models that integrate demographic, clinical, laboratory, treatment, and longitudinal patient-reported outcome data to improve prediction of survival and identify clinically meaningful risk groups. Contemporary prognostic tools are needed to account for evolving treatment patterns, disease heterogeneity, and changes in patient status over time.(Halabi et al., 2023; Halabi et al., 2026)

Third, we will characterize treatment sequencing patterns across contemporary randomized clinical trials and evaluate their associations with  OS and QOL using advanced statistical, causal inference, and machine learning methods. Despite the availability of multiple effective treatment options, there is limited evidence regarding the optimal sequence of therapies following progression. These analyses will provide evidence regarding which treatment sequences are associated with the most favorable clinical outcomes while accounting for differences in patient and disease characteristics.(George et al., 2020; Shore et al., 2021)

The proposed research will generate broadly generalizable scientific knowledge by combining data across multiple international  RCTs, providing greater statistical power, longer follow-up, and greater diversity than any individual study. The findings will inform future clinical trial design, support regulatory evaluation of intermediate clinical endpoints, improve prognostic prediction in mHSPC. Men. "
  ["project_specific_aims"]=>
  string(1625) "This project is a secondary analysis of anonymized individual participant data (IPD) from completed randomized clinical trials in mHSPC. The overall objective is to improve the precision and efficiency of clinical research and treatment decision-making by validating intermediate clinical endpoints, developing prognostic models, and evaluating treatment sequencing strategies.

Aim 1: Validate radiographic progression-free survival (rPFS) and clinical (cPFS) as surrogate endpoints for OS in contemporary mHSPC trials and evaluate additional candidate intermediate clinical endpoints, including time to radiographic progression and freedom from treatment switch. We hypothesize that rPFS and cPFS are valid surrogates for OS in patients receiving modern treatment intensification strategies.

Aim 2: Develop and externally validate prognostic models for OS and rPFS using clinical, disease, and treatment-related variables. We hypothesize that models incorporating on-treatment factors will improve prediction of outcomes and accurately identify distinct prognostic risk groups.

Aim 3: Characterize treatment patterns, treatment duration, and sequencing strategies across contemporary mHSPC trials and determine their association with OS. We hypothesize that outcomes vary according to treatment sequence and patient characteristics.

Aim 4: Evaluate the impact of treatment sequencing on QOL. We hypothesize that treatment sequences have distinct effects on QOL and that these effects vary by prognostic risk group. 

Aim 5: Assess the prognostic importance of baseline and longitudinal QOL."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [1]=>
    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(1807) "This study is a secondary analysis of anonymized individual participant data (IPD) from completed randomized clinical trials in metastatic hormone-sensitive prostate cancer (mHSPC). Data requested through the YODA Project include LATITUDE (NCT01715285) and TITAN (NCT02489318). Additional datasets may include ARCHES (NCT02677896), ARASENS (NCT02799602), and selected trials available through the STOPCaP/ICECaP collaborations, including STAMPEDE (NCT00268476), ENZAMET (NCT02446405), PEACE-1 (NCT01957436), SWOG0925 (NCT01120236) and SWOG1216 (NCT01809691) (Yu et al, 2015; Fizazi et al., 2017, 2019; Davis et al., 2019; Fizazi et al., 2022; Chi et al., 2019; Armstrong et al., 2019; Smith et al., 2022; Attard et al., 2023; Agarwal et al., 2022).

The anticipated pooled dataset will include more than 10,000 men with mHSPC.

Inclusion Criteria 

•	Men enrolled in randomized clinical trials with mHSPC.

Exclusion Criteria: No exclusion criteria .

External datasets will be obtained through existing approved collaborations, including the STOPCaP/ICECaP programs and approved data-sharing agreements with study sponsors and investigators (Sweeney et al., 2015; Halabi et al., 2024). Because the scientific objectives require validation of surrogate endpoints, development of prognostic models, and evaluation of treatment sequencing strategies across trials, the proposed research will involve a pooled IPD meta-analysis.

Subject to sponsor approvals, data-sharing agreements, and Vivli/YODA data-sharing requirements, external anonymized datasets will be uploaded into the approved secure research environment and harmonized with YODA-provided data. Patient-level datasets will then be integrated to create a pooled IPD resource for the proposed analyses.

"
  ["project_main_outcome_measure"]=>
  string(1925) "Primary Outcome Measure

Overall Survival (OS): OS will be the primary outcome measure and will be defined as the time from randomization to death from any cause. Patients who are alive at the time of analysis will be censored at the date of last known follow-up. OS is the accepted gold-standard clinical endpoint and will be used as the reference endpoint for surrogate endpoint validation analyses.

Secondary Outcome Measures

1.	 rPFS: Defined as the time from randomization to radiographic disease progression or death from any cause, whichever occurs first. When study-specific definitions differ, sensitivity analyses using trial-specific definitions will be conducted.

2.	Clinical PFS: Defined as the time from randomization to the first occurrence of clinical or radiographic progression, initiation of a new systemic therapy, symptomatic progression, or death, whichever occurs first. Sensitivity analyses will evaluate study-specific definitions where applicable.

3.	Time to Radiographic Progression (TRP): Defined as the time from randomization to radiographic disease progression.

4.	Freedom from Treatment Switch (FTS): Defined as the time from randomization to the initiation of a subsequent systemic anticancer therapy due to disease progression. Treatment changes solely due to toxicity will not be considered an event.

5.	QOL Outcomes: Patient-reported outcomes collected in the parent trials, including FACT-P, Brief Pain Inventory (BPI), BPI Severity Score (BPI-SFSS), BPI Interference Score (BPI-SFIS), EQ-5D, EORTC QLQ-C30, fatigue measures, and other available QOL assessments. These outcomes will be analyzed both longitudinally and at clinically relevant landmark time points.

No substantive changes to these primary and secondary outcome measures are anticipated in the final analyses as study-specific endpoint definitions will be used.

"
  ["project_main_predictor_indep"]=>
  string(2059) "1. Treatment Assignment (Aim 1 – Surrogate Endpoint Validation)

The primary predictor for surrogate endpoint analyses will be randomized treatment assignment within each clinical trial.

2.  Baseline Clinical and Disease Characteristics (Aim 2)

For prognostic model development, candidate independent variables include:

•	Age, ECOG performance status, disease volume (high vs low volume), De novo versus recurrent metastatic disease, T stage, N stage, Gleason score, PSA, Hemoglobin. alkaline phosphatase, Lactate dehydrogenase (LDH), Presence of visceral metastases, and xtent of bone metastases.

On-Treatment Clinical Factors (Aim 2)

Time-varying predictors will include changes in laboratory values, disease burden, treatment exposure, progression status, and other clinically relevant factors collected during follow-up. These variables will be evaluated for their ability to improve prediction of overall survival and progression outcomes.

3. Treatment Sequence and Treatment Duration (Aims 3 and 4)

The primary predictor for treatment-sequencing analyses will be the sequence of systemic therapies received following randomization. Treatment sequence will be modeled as a time-varying exposure and characterized by:

•	Initial treatment received, subsequent therapies administered after progression, order of therapies received, duration of each therapy and number of treatment lines received.

5. QOL (Aim 5) will include baseline and longitudinal patient-reported outcomes, including FACT-P, Brief Pain Inventory (BPI), EQ-5D, EORTC QLQ-C30, fatigue measures, and other available QOL assessments. These measures will be analyzed as continuous variables and, where appropriate, categorized according to established clinically meaningful thresholds.



These predictors will be used to evaluate their associations with overall survival, progression-related endpoints, prognostic risk classification, treatment sequencing outcomes, and quality-of-life outcomes.

"
  ["project_other_variables_interest"]=>
  string(702) "Additional variables will be used to characterize the study population, assess heterogeneity across trials, and support multivariable adjustment.

Demographic: Geographic region, Race/ethnicity, where available.

Clinical Variables: Body mass index, where available, Prior local therapy (prostatectomy and/or radiotherapy), Comorbidity variables available across studies.

Longitudinal Variables

•	Changes in laboratory measurements (PSA, alkaline phosphatase, hemoglobin, LDH) during follow-up.

•	Changes in disease status during follow-up.

•	Time-varying treatment exposure variables.

•	Longitudinal changes in QOL measures from baseline.

"
  ["project_stat_analysis_plan"]=>
  string(3816) "For Aim 1, validation of rPFS, cPFS, TRP, and FTS as surrogate endpoints for OS will utilize a two-stage IPD meta-analytic framework based on established approaches for surrogate endpoint evaluation (Buyse et al., 2000; Buyse et al., 2016; Burzykowski and Buyse, 2006; Halabi et al., 2024; Xie et al., 2019). Patient-level associations between candidate surrogate endpoints and OS will be evaluated using copula-based methods (Owzar et al., 2007; Jiang et al., 2005) and trial-level associations between treatment effects on surrogate endpoints and treatment effects on OS will be evaluated using weighted regression models. Surrogate threshold effects and coefficients of determination will be estimated to assess the strength of surrogacy.

For Aim 2, multivariable prognostic models for OS and rPFS will be developed and externally validated using demographic, clinical, laboratory, treatment, and disease characteristics. Cox proportional hazards models (Cox, 1972), random survival forests (Ishwaran et al., 2008), and survival neural network approaches (Katzman et al., 2018; Kvamme et al., 2019; Ching et al., 2018) will be evaluated. Model performance will be assessed using the c-index, time-dependent area under the receiver operating characteristic curve (Uno et al., 2007), calibration analyses (Harrell, 2015), and external validation procedures.

For Aim 3, treatment sequence will be analyzed as a time-varying exposure that incorporates treatment order, duration, and subsequent therapies. Time-dependent Cox models (Cox, 1972) will be used to evaluate associations between treatment sequences and OS. To address treatment switching and time-dependent confounding, causal inference approaches including augmented inverse probability weighted estimators (Zhang and Zhao, 2012), marginal structural models (Robins et al., 2000), inverse probability weighting (Robins and Finkelstein, 2000), and rank-preserving structural failure time models (Robins and Tsiatis, 1991) will be explored. Additional analyses may utilize Bayesian hierarchical models and machine learning approaches including random survival forests (Ishwaran et al., 2008), survival neural networks (Katzman et al., 2018), and point-process methods (Weiss and Page, 2013; Zhang et al., 2020) to investigate treatment heterogeneity and identify subgroups with distinct survival patterns.

For Aim 4, longitudinal QOL outcomes, including FACT-P, BPI, EQ-5D, EORTC QLQ-C30, fatigue measures, and other available patient-reported outcomes, will be analyzed using generalized estimating equations (Liang and Zeger, 1986), mixed-effects models, and functional regression methods (Ramsay and Silverman, 2005). Associations between treatment sequence and QOL trajectories will be evaluated over time.

For Aim 5, the prognostic value of baseline and longitudinal QOL measures for OS will be assessed using joint models of longitudinal and survival outcomes (Tsiatis and Davidian, 2004; Tsiatis et al., 1995). Associations between prognostic risk groups, QOL trajectories, and survival outcomes will also be evaluated.

Missing data will be handled using multiple imputation where appropriate (Rubin, 1987). Sensitivity analyses will evaluate the robustness of findings to alternative endpoint definitions, missing data assumptions, and between-study heterogeneity. Analyses will be conducted using the R statistical computing environment and validated R packages available within the approved secure research environment. Model development, survival analyses, surrogate endpoint validation, causal inference analyses, and machine learning methods will be implemented using established and validated R packages consistent with current best practices for clinical trial and IPD meta-analysis research.



"
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      ["value"]=>
      string(6) "python"
      ["label"]=>
      string(6) "Python"
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      ["value"]=>
      string(7) "rstudio"
      ["label"]=>
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  ["project_timeline"]=>
  string(1672) "The proposed project will be completed over a 24-month period following approval of data access and execution of all required data-sharing agreements.

Year 1 (Months 1–12): Surrogate Endpoint Validation, Prognostic Modeling, and Treatment Sequencing

•	Months 1–3: Data acquisition, harmonization, quality control, and creation of the pooled IPD dataset.

•	Months 3–6: Completion of analyses for Aim 1 (validation of rPFS, cPFS, TRP, and FTS as surrogate endpoints for OS).

•	Months 6–9: Development and validation of prognostic models and prognostic risk groups (Aim 2).

•	Months 9–12: Completion of treatment pattern and treatment sequencing analyses (Aim 3).

•	End of Year 1: Preparation of abstracts and drafting of manuscripts related to Aims 1–3.

Year 2 (Months 13–24): Quality of Life and Longitudinal Prognostic Analyses

•	Months 13–18: Completion of analyses evaluating the impact of treatment sequencing on QOL (Aim 4).

•	Months 18–21: Completion of analyses evaluating the prognostic importance of baseline and longitudinal QOL measures for OS (Aim 5).

•	Months 21–22: Integration of findings across study aims and completion of all planned analyses.

•	Months 22–23: Preparation and submission of primary and secondary manuscripts to peer-reviewed journals.

•	Month 24: Submission of the final study report and results summary to the YODA Project.

This timeline reflects the complexity of harmonizing multiple contemporary mHSPC clinical trial datasets and conducting pooled IPD analyses across more than 10,000 patients.

"
  ["project_dissemination_plan"]=>
  string(1497) "We anticipate generating multiple manuscripts corresponding to the major aims of the project. The first manuscript will focus on validation of rPFS, cPFS, TRP, and FTS as surrogate endpoints for OS in contemporary mHSPC trials. A second manuscript will report development and validation of prognostic models for OS and rPFS. Additional manuscripts will evaluate treatment sequencing strategies and their associations with OS and QOL, as well as the prognostic importance of longitudinal QOL outcomes.

Findings will be submitted to high-impact peer-reviewed journals, including Journal of Clinical Oncology, European Urology, Lancet Oncology, JAMA Oncology, Annals of Oncology, Clinical Cancer Research, and European Urology Focus, depending on the scope and results of individual analyses.

Results will also be presented at major scientific meetings including the American Society of Clinical Oncology (ASCO) Annual Meeting, the European Society for Medical Oncology (ESMO) Congress, and other relevant oncology and clinical trial methodology conferences.

In accordance with YODA and data-sharing policies, summary study findings and resulting publications will be reported to the YODA Project. The knowledge generated from this pooled IPD analysis is expected to improve clinical trial design, support validation of surrogate endpoints, enhance prognostic risk stratification, inform treatment sequencing decisions, and ultimately improve outcomes for men with mHSPC.

"
  ["project_bibliography"]=>
  string(7863) "

Agarwal N, Tangen CM, Hussain MHA, et al. Orteronel for Metastatic Hormone-Sensitive Prostate Cancer: A Multicenter, Randomized, Open-Label Phase III Trial (SWOG1216). J Clin Oncol. 2022;40:3301-3309.
Armstrong AJ, Iguchi T, Azad AA, et al. Final Overall Survival Analysis from ARCHES. Ann Oncol. 2021;32:S1300-S1301.
Armstrong AJ, Szmulewitz RZ, Petrylak DP, et al. ARCHES: A Randomized, Phase III Study of Androgen Deprivation Therapy With Enzalutamide or Placebo in Men With Metastatic Hormone-Sensitive Prostate Cancer. J Clin Oncol. 2019;37:2974-2986.
Attard G, Murphy L, Clarke NW, et al. Abiraterone acetate plus prednisolone with or without enzalutamide for patients with metastatic prostate cancer starting androgen deprivation therapy: final results from two randomized phase III trials of the STAMPEDE platform. Lancet Oncol. 2023;24:443-456.
Buyse M, Molenberghs G, Burzykowski T, et al. The validation of surrogate endpoints in meta-analyses of randomized experiments. 2000;1:49-67.
Buyse M, Molenberghs G, Paoletti X, et al. Statistical evaluation of surrogate endpoints with examples from cancer clinical trials. Biometrical Journal. 2016;58:104-132.
Burzykowski T, Buyse M. Surrogate threshold effect: An alternative measure for meta-analytic surrogate endpoint validation. Pharmaceutical Statistics. 2006;5:173-186.
Ching T, Zhu X, Garmire LX. Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput Biol. 2018;14:e1006076.
Chi KN, Agarwal N, Bjartell A, et al. Apalutamide for Metastatic, Castration-Sensitive Prostate Cancer. N Engl J Med. 2019;381:13-24.
Chi KN, Chowdhury S, Bjartell A, et al. Final Survival Analysis of the TITAN Study. J Clin Oncol. 2021;39:2294-2303.
Cox DR. Regression Models and Life-Tables. J R Stat Soc B. 1972;34:187-220.
Davis ID, Martin AJ, Stockler MR, et al. Enzalutamide with Standard First-Line Therapy in Metastatic Prostate Cancer. N Engl J Med. 2019;381:121-131.
Fizazi K, Foulon S, Carles J, et al. Abiraterone plus prednisone added to androgen deprivation therapy and docetaxel in de novo metastatic castration-sensitive prostate cancer (PEACE-1). 2022;399:1695-1707.
Fizazi K, Tran N, Fein L, et al. Abiraterone acetate plus prednisone in patients with newly diagnosed high-risk metastatic castration-sensitive prostate cancer (LATITUDE): final overall survival analysis of a randomised, double-blind, phase 3 trial. Lancet Oncol. 2019;20:686-700.
Fizazi K, Tran N, Fein L, et al. Abiraterone Plus Prednisone in Metastatic, Castration-Sensitive Prostate Cancer. N Engl J Med. 2017;377:352-360.
George DJ, Sartor O, Miller K, et al. Treatment Patterns and Outcomes in Patients With Metastatic Castration-Resistant Prostate Cancer in a Real-World Clinical Practice Setting in the United States. Clin Genitourin Cancer. 2020;18:284-294.
Halabi S, Luo B, Yu C, et al. Stratifying Risk and Treatment Benefit: A Model Predicting Overall Survival in Men with Metastatic De Novo Hormone-Sensitive Prostate Cancer in Trials Investigating Docetaxel (STOPCaP Collaboration). Eur Urol Focus.
Halabi S, Roy A, Rydzewska L, et al. Radiographic Progression-Free Survival and Clinical Progression-Free Survival as Potential Surrogates for Overall Survival in Men With Metastatic Hormone-Sensitive Prostate Cancer. J Clin Oncol. 2024;42:1044-1054.
Halabi S, Yang Q, Roy A, et al. External Validation of a Prognostic Model of Overall Survival in Men With Chemotherapy-Naive Metastatic Castration-Resistant Prostate Cancer. J Clin Oncol. 2023;41:2736-2746.
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Ishwaran H, Kogalur UB, Blackstone EH, et al. Random survival forests. Ann Appl Stat. 2008;2:841-860.
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Katzman JL, Shaham U, Cloninger A, et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18:24.
Kvamme H, Borgan O, Scheel I. Time-to-Event Prediction with Neural Networks and Cox Regression. J Mach Learn Res. 2019;20:1-30.
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Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability weighted log-rank tests. 2000;56:779-788.
Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. 2000;11:550-560.
Robins JM, Tsiatis AA. Correcting for non-compliance in randomized trials using rank preserving structural failure time models. Commun Stat Theory Methods. 1991;20:2609-2631.
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