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      string(210) "NCT02489318 - A Phase 3 Randomized, Placebo-controlled, Double-blind Study of Apalutamide Plus Androgen Deprivation Therapy (ADT) Versus ADT in Subjects With Metastatic Hormone-sensitive Prostate Cancer (mHSPC)"
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  ["project_title"]=>
  string(150) "Deconstructing Patient-Reported Outcomes Using Bayesian Transition Models in Metastatic Prostate Cancer Receiving Androgen Receptor Pathway Inhibitors"
  ["project_narrative_summary"]=>
  string(518) "This study examines how treatments that extended survival in advanced prostate cancer (apalutamide, abiraterone) from the TITAN and LATITUDE trials affect patients' quality of life. Using Bayesian ordinal transition modeling to analyze changes in pain, fatigue, and functioning over time, we aim to understand symptom progression patterns. Results will inform clinical decision-making by revealing how treatments impact both survival and daily living, helping balance survival with quality of life for future patients."
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    ["first_name"]=>
    string(6) "Wataru"
    ["last_name"]=>
    string(9) "Fukuokaya"
    ["degree"]=>
    string(2) "MD"
    ["primary_affiliation"]=>
    string(39) "The Jikei University School of Medicine"
    ["email"]=>
    string(20) "wfukuokaya@gmail.com"
    ["state_or_province"]=>
    string(5) "Tokyo"
    ["country"]=>
    string(5) "Japan"
  }
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      ["p_pers_f_name"]=>
      string(9) "Keiichiro"
      ["p_pers_l_name"]=>
      string(4) "Mori"
      ["p_pers_degree"]=>
      string(7) "MD, PhD"
      ["p_pers_pr_affil"]=>
      string(39) "The Jikei University School of Medicine"
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      string(8) "Takahiro"
      ["p_pers_l_name"]=>
      string(6) "Kimura"
      ["p_pers_degree"]=>
      string(7) "MD, PhD"
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      string(39) "The Jikei University School of Medicine"
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      string(7) "Akihiro"
      ["p_pers_l_name"]=>
      string(8) "Hirakawa"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(26) "Institute of Science Tokyo"
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    ["label"]=>
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  ["project_date_type"]=>
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  ["property_scientific_abstract"]=>
  string(1700) "Background

Randomized controlled trials have shown that adding androgen receptor pathway inhibitors (ARPIs) like abiraterone acetate plus prednisone (AAP) and apalutamide to androgen deprivation therapy (ADT) maintains health-related quality of life (HRQoL) in patients with metastatic castration-sensitive prostate cancer (mCSPC). Traditional analyses often underutilize the ordinal structure of patient-reported outcome (PRO) data. Bayesian ordinal transition models can better leverage this structure to capture nuanced treatment effects.



Objective

To conduct post-hoc analysis of PRO data from randomized trials comparing ARPIs with placebo (both with ADT) in patients with mCSPC.



Study design

Post-hoc analysis of data from placebo-controlled, double-blinded randomized clinical trials.



Participants

Patients with mCSPC who received either ARPIs (AAP or apalutamide) or placebo in the LATITUDE and TITAN trials.



Primary and secondary measures

Longitudinal PRO data collected using validated instruments: Brief Pain Inventory, Brief Fatigue Inventory, Functional Assessment of Cancer Therapy–Prostate, and EuroQoL 5D questionnaire 5 level.



Statistical analysis

Bayesian ordinal transition models will be implemented to analyze PRO state transitions over time. This analytical approach will quantify the effects of AAP and apalutamide on transitions between PRO states and derive clinically meaningful benefit measures. Additionally, the analysis will explore how baseline patient characteristics and baseline PRO scores influence the effect of ARPIs on subsequent PRO outcomes."
  ["project_brief_bg"]=>
  string(1297) "The addition of androgen receptor pathway inhibitors (ARPIs) such as abiraterone acetate plus prednisone (AAP) and apalutamide has shown improved overall survival in patients with castration-sensitive prostate cancer. However, a comprehensive understanding of the effect of ARPIs on symptom experience and health-related quality of life of patients receiving ARPI therapy remains elusive. The TITAN and LATITUDE trials collected rich patient-reported outcome data using validated instruments (Brief Pain Inventory [BPI], Brief Fatigue Inventory [BFI], Functional Assessment of Cancer Therapy – Prostate [FACT-P], and EuroQoL 5D questionnaire 5 level [EQ-5D-5L]), providing an opportunity for deeper analysis of treatment effects on patient experience (Chi et al., 2019; Fizazi et al., 2017).

This project addresses a significant gap in PRO analysis methodology for oncology trials. Clinically, understanding how treatments influence transitions between symptom states provides actionable information for clinical practice. Bayesian ordinal transition modeling represents the patient experience as a series of transitions between discrete health states, offering a natural framework for analyzing the trajectory of symptoms and functioning throughout cancer treatment (Rohde et al., 2024)."
  ["project_specific_aims"]=>
  string(428) "Our first aim is to implement Bayesian ordinal transition models to analyze longitudinal pain, fatigue, functional status, and quality of life data from the TITAN and LATITUDE trials. Our second aim is to quantify the effects of AAP and apalutamide on PRO state transitions and derive clinical benefit measures. Our third aim is to explore the effect of baseline characteristics and baseline PROs on the effect of ARPIs on PROs."
  ["project_study_design"]=>
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    ["label"]=>
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  ["project_purposes"]=>
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      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
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  ["project_research_methods"]=>
  string(183) "This study will include the intention-to-treat population from the TITAN and LATITUDE trials. Participants who were not included in this population will be excluded from the analysis."
  ["project_main_outcome_measure"]=>
  string(197) "Primary outcomes comprise PRO data collected throughout the follow-up period. PROs will be measured using the BPI, BFI, FACT-P, and EQ-5D-5L. No secondary outcomes have been defined for this study."
  ["project_main_predictor_indep"]=>
  string(261) "The main predictor is randomized treatment assignment. For the TITAN trial, this is apalutamide plus androgen deprivation therapy (ADT) versus placebo plus ADT. For the LATITUDE trial, this is abiraterone acetate plus prednisone and ADT versus placebo plus ADT."
  ["project_other_variables_interest"]=>
  string(131) "Baseline characteristics include age and Eastern Cooperative Oncology Group (ECOG) performance status at the time of randomization."
  ["project_stat_analysis_plan"]=>
  string(2660) "The following statistical analysis will be implemented using R and Stan.



1. Analytical modeling

We will fit the Bayesian ordinal transition model. The ordinal outcomes on each treatment cycle are modeled as a function of the previous state, randomized treatment assignment (ARPI or placebo), study cycle (modeled as a restricted cubic spline), age (modeled as a restricted cubic spline) and ECOG performance status. Because each PRO measure is confounded for the different set of baseline characteristics, we define the set of baseline characteristics for covariates of model for each PRO measure. If a continuous variable is categorized during the data anonymization process, we will handle age as the categorical variable. Both clinical trials (TITAN and LATITUDE) structure cycles as 28-day periods with PRO tracking continuing 12 months beyond treatment discontinuation. We will also include a randomized treatment assignment × study cycle interaction so that the effect of ARPI can vary over time. Each PRO measure will be categorized if necessary or clinically meaningful.



2. Posterior distribution assessment

The rmsb package will facilitate Markov Chain Monte Carlo sampling within R and Stan (Harrell, 2024). We assume log-odds ratios follow a normal distribution. Chain convergence will be evaluated through visual examination of trace plots. Distribution characteristics will be summarized using median posterior values and 95% posterior intervals.



3. Transition probabilities evaluation

Temporal patterns in treatment effects on state transition probabilities will be visualized across treatment cycles. These transition probabilities represent the probability of a patient's movement between clinical states across consecutive assessment periods. For instance, we will calculate the probability of a patient transitioning from a pain score of 3 at cycle 3 to a score of 2 at cycle 4 on the Brief Pain Inventory scale.



4. Therapeutic benefit quantification

The model will generate several derivative metrics. We will first calculate state occupancy probabilities (SOPs) across treatment cycles for each outcome measure, representing the probability of a patient having a particular clinical state at a specific timepoint. Using these SOPs, we will determine posterior differences in mean time in PRO state between treatment arms and time benefit of ARPI compared to placebo. Additionally, we will investigate potential associations between these benefit metrics and baseline PRO measures and baseline characteristics including age and ECOG performance status."
  ["project_software_used"]=>
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    ["label"]=>
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  ["project_timeline"]=>
  string(481) "The project will span approximately 12 months. During months 1-2, we will focus on data acquisition and processing of the PRO instruments. Months 3-6 will involve implementation of Bayesian ordinal transition models for each PRO domain and preliminary interpretation of transition patterns. Manuscript writing and preparation will occur during months 7-8. The final four months will be dedicated to responding to reviewer comments, refining analyses, and finalizing the manuscript."
  ["project_dissemination_plan"]=>
  string(381) "We plan to share findings from this research through multiple channels. Our primary output will be a manuscript for submission to a peer-reviewed oncology journal. In addition to publication, we will present our findings at major oncology conferences, including the American Society of Clinical Oncology (ASCO) Annual Meeting and the European Association of Urology (EAU) Congress."
  ["project_bibliography"]=>
  string(2151) "
Chi, K. N., Agarwal, N., Bjartell, A., Chung, B. H., Pereira de Santana Gomes, A. J., Given, R., Juárez Soto, Á., Merseburger, A. S., Özgüroğlu, M., Uemura, H., Ye, D., Deprince, K., Naini, V., Li, J., Cheng, S., Yu, M. K., Zhang, K., Larsen, J. S., McCarthy, S., … TITAN Investigators. (2019). Apalutamide for metastatic, castration-sensitive prostate cancer. The New England Journal of Medicine, 381(1), 13–24.
Fizazi, K., Tran, N., Fein, L., Matsubara, N., Rodriguez-Antolin, A., Alekseev, B. Y., Özgüroğlu, M., Ye, D., Feyerabend, S., Protheroe, A., De Porre, P., Kheoh, T., Park, Y. C., Todd, M. B., Chi, K. N., & LATITUDE Investigators. (2017). Abiraterone plus prednisone in metastatic, castration-sensitive prostate cancer. The New England Journal of Medicine, 377(4), 352–360.
Harrell, F. (2024, March 15). rmsb: Bayesian Regression Modeling Strategies. https://hbiostat.org/R/rmsb/
Rohde, M. D., French, B., Stewart, T. G., & Harrell, F. E., Jr. (2024). Bayesian transition models for ordinal longitudinal outcomes. Statistics in Medicine, 43(18), 3539–3561.
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    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(17161)
    ["id"]=>
    int(17161)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0188_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0188_site.pdf"
    ["filesize"]=>
    int(1315549)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/03/YODA-Project-Review-2025-0188_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0188/yoda-project-review-2025-0188_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0188_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(16870)
    ["date"]=>
    string(19) "2025-04-29 18:40:37"
    ["modified"]=>
    string(19) "2025-04-29 18:40:37"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["request_overridden_res"]=>
  string(1) "3"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(1) {
  [0]=>
  string(5) "Japan"
}


pi affil
array(1) {
  [0]=>
  string(8) "Academia"
}


products
array(2) {
  [0]=>
  string(6) "zytiga"
  [1]=>
  string(7) "erleada"
}


num of trials
array(1) {
  [0]=>
  string(1) "2"
}


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