array(41) {
  ["request_overridden_res"]=>
  string(1) "3"
  ["project_status"]=>
  string(30) "approved_pending_dua_signature"
  ["project_assoc_trials"]=>
  array(1) {
    [0]=>
    object(WP_Post)#4630 (24) {
      ["ID"]=>
      int(8013)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2023-08-05 04:44:39"
      ["post_date_gmt"]=>
      string(19) "2023-08-05 04:44:39"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(170) "NCT01946204 - A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Phase III Study of ARN-509 in Men With Non-Metastatic (M0) Castration-Resistant Prostate Cancer"
      ["post_excerpt"]=>
      string(0) ""
      ["post_status"]=>
      string(7) "publish"
      ["comment_status"]=>
      string(6) "closed"
      ["ping_status"]=>
      string(6) "closed"
      ["post_password"]=>
      string(0) ""
      ["post_name"]=>
      string(162) "nct01946204-a-multicenter-randomized-double-blind-placebo-controlled-phase-iii-study-of-arn-509-in-men-with-non-metastatic-m0-castration-resistant-prostate-cancer"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-04-30 16:21:45"
      ["post_modified_gmt"]=>
      string(19) "2025-04-30 20:21:45"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(211) "https://dev-yoda.pantheonsite.io/clinical-trial/nct01946204-a-multicenter-randomized-double-blind-placebo-controlled-phase-iii-study-of-arn-509-in-men-with-non-metastatic-m0-castration-resistant-prostate-cancer/"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
  }
  ["project_title"]=>
  string(73) "Individualized Prediction of Survival Benefit From Apalutamide in SPARTAN"
  ["project_narrative_summary"]=>
  string(534) "This project will use de-identified individual patient-level data from the SPARTAN trial to develop prediction models and estimate heterogeneous treatment effects of apalutamide. The goal is to quantify individualized survival benefit, metastasis-related benefit, and adverse-event risk, and to identify patient groups with favorable or less favorable net clinical benefit. By moving beyond average treatment effects, this work may help refine risk stratification and support more personalized treatment decisions for men with nmCRPC."
  ["project_learn_source"]=>
  string(12) "scien_public"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(7) "Junping"
    ["last_name"]=>
    string(4) "Yang"
    ["degree"]=>
    string(2) "MD"
    ["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"
  }
  ["project_key_personnel"]=>
  bool(false)
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(3) "yes"
    ["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(1424) "Background: Apalutamide improves metastasis-free survival in men with nonmetastatic castration-resistant prostate cancer, but individual benefit and toxicity risk may vary.

Objective: To develop prediction models for individualized benefit-risk assessment and estimate heterogeneous treatment effects of apalutamide.

Study Design: Secondary analysis of de-identified individual patient-level data from the randomized, double-blind, placebo-controlled phase 3 SPARTAN trial.

Participants: Men enrolled in SPARTAN with nonmetastatic castration-resistant prostate cancer and prostate-specific antigen doubling time ≤10 months, randomized to apalutamide or placebo plus androgen-deprivation therapy.

Primary and Secondary Outcome Measure(s): Primary outcomes are individualized predicted overall survival benefit and heterogeneous treatment effects. Secondary outcomes include metastasis-free survival, time to metastasis, progression-free survival, time to symptomatic progression, PSA progression, selected adverse events, serious adverse events, and treatment discontinuation due to adverse events.

Statistical Analysis: Cox models, penalized regression, flexible survival models, and machine-learning methods will estimate risk, benefit, and treatment-effect heterogeneity. Model performance will be assessed by discrimination, calibration, internal validation, and sensitivity analyses."
  ["project_brief_bg"]=>
  string(2411) "Nonmetastatic castration-resistant prostate cancer (nmCRPC) is an important clinical disease state in which patients have rising prostate-specific antigen (PSA) levels despite castrate testosterone levels but no detectable distant metastasis on conventional imaging. Patients with a short PSA doubling time are at particularly high risk for progression to metastatic disease and death. Preventing or delaying metastasis is therefore a major therapeutic goal, as metastases are associated with pain, skeletal-related events, reduced quality of life, subsequent systemic therapy, and mortality.



The SPARTAN trial established apalutamide as an effective treatment for men with high-risk nmCRPC. In this randomized, double-blind, placebo-controlled phase 3 trial, apalutamide significantly prolonged metastasis-free survival compared with placebo when added to ongoing androgen-deprivation therapy. The study also demonstrated improvements in several clinically relevant endpoints, including time to metastasis, progression-free survival, time to symptomatic progression, and PSA-related outcomes. These findings substantially changed the treatment landscape for nmCRPC.



However, treatment decisions in nmCRPC remain complex. Although apalutamide provides clear benefit at the trial-population level, individual patients may differ in both expected benefit and risk of treatment-related adverse events. Factors such as age, PSA kinetics, nodal status, baseline disease burden, functional status, comorbidity, bone health, and other clinical characteristics may influence survival benefit, metastasis risk, and toxicity. Key adverse events associated with apalutamide, including rash, fatigue, falls, fractures, hypothyroidism, and treatment discontinuation, are particularly relevant for individualized decision-making.



The significance of this project is its potential to materially enhance generalizable medical knowledge regarding precision treatment in prostate cancer. The results may provide clinically interpretable tools or evidence frameworks to help clinicians and patients balance expected efficacy against potential harm. Such knowledge could improve shared decision-making, reduce avoidable toxicity, optimize treatment selection, and inform future trial design, guideline development, and public health strategies for high-risk prostate cancer populations."
  ["project_specific_aims"]=>
  string(862) "This project aims to determine whether the benefits and harms of apalutamide in men with nonmetastatic castration-resistant prostate cancer vary according to baseline patient and disease characteristics. Using de-identified individual patient-level data from the SPARTAN randomized trial, we will pursue three specific aims.

Aim 1: Develop and validate prediction models for baseline risk of overall survival, metastasis-free survival, and clinically important adverse events.

Aim 2: Estimate heterogeneous treatment effects of apalutamide versus placebo on survival and metastasis-related outcomes across clinically defined and model-derived patient subgroups.

Aim 3: Integrate predicted treatment benefit and toxicity risk to characterize individualized net clinical benefit and identify patients most likely to benefit or experience harm."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
  }
  ["project_purposes"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(1388) "The data source will be de-identified individual patient-level data from the SPARTAN trial made available through the YODA Project. SPARTAN was a randomized, double-blind, placebo-controlled phase 3 trial evaluating apalutamide versus placebo, each in combination with ongoing androgen-deprivation therapy, in men with nonmetastatic castration-resistant prostate cancer.

The study sample for this secondary analysis will include all randomized SPARTAN participants available in the YODA dataset who meet the original trial population definition: adult men with histologically or cytologically confirmed prostate cancer; castration-resistant disease with ongoing androgen-deprivation therapy or prior bilateral orchiectomy; no distant metastasis on conventional imaging at baseline; prostate-specific antigen doubling time of 10 months or less; and available baseline covariate, treatment assignment, and follow-up outcome data sufficient for the planned analyses.

Exclusion criteria for this analysis will be limited to participants without essential data needed to define treatment assignment, baseline eligibility variables, or primary outcomes. Patients with missing nonessential covariates will not be excluded solely for missingness; missing data will be addressed using appropriate statistical methods such as multiple imputation or complete-case sensitivity analyses."
  ["project_main_outcome_measure"]=>
  string(1614) "The primary outcome measures will be individualized predicted overall survival benefit and heterogeneous treatment effect of apalutamide versus placebo on overall survival. Overall survival will be defined as time from randomization to death from any cause; participants alive at last follow-up will be censored at the last known alive date. Individualized treatment benefit will be estimated as the difference in predicted survival outcomes under apalutamide versus placebo at prespecified time points and/or as treatment effect on the hazard scale.

Secondary outcome measures will include metastasis-free survival, defined as time from randomization to radiographic distant metastasis or death from any cause, whichever occurs first; time to distant metastasis; progression-free survival; time to symptomatic progression; time to PSA progression, defined according to the SPARTAN trial criteria where available; and adverse event outcomes. Adverse events of interest will include rash, fatigue, falls, fractures, hypothyroidism, grade 3 or higher adverse events, serious adverse events, and treatment discontinuation due to adverse events. Adverse event outcomes will be analyzed as binary occurrence during the treatment/follow-up period and, where data permit, as time-to-first-event outcomes.

A composite net clinical benefit measure will be derived by integrating predicted survival or metastasis-related benefit with predicted risk of clinically important adverse events. This composite will be used for exploratory analyses and will be clearly labeled as exploratory in any final publication."
  ["project_main_predictor_indep"]=>
  string(1864) "The main independent variable will be randomized treatment assignment: apalutamide plus ongoing androgen-deprivation therapy versus placebo plus ongoing androgen-deprivation therapy. Treatment will be analyzed according to the intention-to-treat principle and coded as a binary variable, with placebo as the reference group.

For heterogeneous treatment-effect analyses, the key predictors will be baseline patient and disease characteristics measured before randomization. These may include age, race/ethnicity where available, geographic region, Eastern Cooperative Oncology Group performance status, baseline prostate-specific antigen (PSA), PSA doubling time, Gleason score or grade group, prior local therapy, prior systemic therapy, baseline nodal status, presence of pelvic lymph nodes, baseline pain or symptom measures if available, comorbidity-related variables, body mass index, laboratory values, and bone-health or fracture-risk indicators where available.

Continuous variables will be modeled using clinically meaningful categories where prespecified and flexible functional forms, such as restricted cubic splines, where appropriate. Examples include age as continuous and/or categorized groups, PSA doubling time as continuous and/or clinically defined risk categories, and baseline PSA as log-transformed or categorized by quantiles. Categorical variables will be represented using indicator variables.

Treatment-by-covariate interactions will be used to evaluate whether baseline characteristics modify the effect of apalutamide on primary and secondary outcomes. In machine-learning analyses, baseline covariates will be used as candidate effect modifiers to estimate individualized treatment effects. All predictor definitions and transformations will be prespecified before model fitting and reported in the final analysis."
  ["project_other_variables_interest"]=>
  string(1935) "Other variables of interest will include baseline demographic, clinical, disease, treatment history, laboratory, and safety-related variables available in the SPARTAN dataset. These variables will be used to characterize the study sample, support multivariable risk adjustment, and develop prediction models.



Demographic variables may include age at randomization, race/ethnicity where available, region/country, height, weight, and body mass index. Clinical variables may include Eastern Cooperative Oncology Group performance status, baseline symptoms or pain measures, medical history, comorbid conditions, prior falls or fractures where available, and concomitant medications relevant to bone health or adverse-event risk. Disease-related variables may include baseline prostate-specific antigen, PSA doubling time, Gleason score or grade group, time since initial prostate cancer diagnosis, prior prostatectomy or radiotherapy, prior hormonal or systemic therapies, baseline nodal status, measurable pelvic lymph nodes, and baseline imaging findings confirming nonmetastatic disease.

Laboratory variables may include hemoglobin, alkaline phosphatase, lactate dehydrogenase, albumin, creatinine, liver function tests, testosterone if available, and other baseline laboratory values relevant to prognosis or toxicity. Treatment-related variables may include randomized treatment arm, treatment exposure duration, dose interruptions, dose reductions, and discontinuation status; post-randomization treatment variables will be used descriptively or in sensitivity analyses, not as baseline predictors of treatment effect.

Continuous variables will be summarized using means/standard deviations or medians/interquartile ranges and modeled continuously, with transformations or splines as appropriate. Categorical variables will be summarized as counts and percentages and modeled using indicator variables."
  ["project_stat_analysis_plan"]=>
  string(5076) "All analyses will use de-identified individual patient-level data from the SPARTAN randomized trial and will follow the intention-to-treat principle unless otherwise specified. The analysis population will include all randomized participants with treatment assignment and outcome data sufficient for the planned analyses.



Baseline characteristics will be summarized overall and by randomized treatment arm. Continuous variables will be described using means and standard deviations or medians and interquartile ranges, as appropriate. Categorical variables will be described using counts and percentages. Balance between treatment arms will be assessed descriptively using standardized mean differences rather than relying primarily on hypothesis testing.



Time-to-event outcomes, including overall survival, metastasis-free survival, time to distant metastasis, progression-free survival, time to symptomatic progression, and time to PSA progression, will be analyzed using Kaplan-Meier methods and Cox proportional hazards models. Median event-free times, event probabilities at clinically relevant time points, hazard ratios, 95% confidence intervals, and log-rank tests will be reported where appropriate. The proportional hazards assumption will be evaluated using Schoenfeld residuals and visual inspection of log-minus-log plots. If the assumption is violated, flexible parametric survival models, time-varying coefficients, or restricted mean survival time analyses will be considered.



Adverse event outcomes, including rash, fatigue, falls, fractures, hypothyroidism, grade 3 or higher adverse events, serious adverse events, and treatment discontinuation due to adverse events, will be analyzed as binary outcomes using logistic regression and, where dates are available, as time-to-first-event outcomes using Cox models. Risk differences, odds ratios or hazard ratios, and 95% confidence intervals will be reported. Competing risk methods may be considered for selected adverse event or metastasis endpoints if death without the event is a clinically important competing event.



Multivariable models will adjust for prespecified baseline covariates, including age, race/ethnicity where available, geographic region, ECOG performance status, baseline PSA, PSA doubling time, Gleason score or grade group, prior local therapy, prior systemic therapy, baseline nodal status, baseline symptoms or pain measures if available, comorbidity-related variables, body mass index, and relevant laboratory values. Continuous predictors will be modeled using clinically meaningful transformations, logarithmic transformations, or restricted cubic splines when appropriate. Penalized regression methods may be used to reduce overfitting when the number of candidate predictors is large relative to the number of events.



To evaluate heterogeneous treatment effects, treatment-by-covariate interaction terms will be added to Cox, logistic, and flexible survival models. Prespecified effect modifiers will include age, PSA doubling time, baseline PSA, ECOG performance status, nodal status, Gleason score or grade group, prior therapy, and baseline laboratory or comorbidity markers. Results will be reported as interaction estimates, subgroup-specific treatment effects, and absolute risk differences or survival differences at prespecified time points. Because interaction analyses may have limited power, findings will be interpreted as exploratory unless strongly supported by consistency and precision.



Advanced prediction and machine-learning analyses may include penalized Cox regression, random survival forests, gradient boosting, causal forests, or related methods to estimate individualized baseline risk and individualized treatment effects. Model performance will be evaluated using discrimination, calibration, and clinical utility. Discrimination will be assessed using the concordance index for survival outcomes and the area under the receiver operating characteristic curve for binary outcomes. Calibration will be examined using calibration plots, observed-versus-predicted risk summaries, and calibration slope/intercept where applicable. Decision curve analysis may be used to evaluate potential clinical utility across relevant threshold probabilities.

A net clinical benefit framework will be developed by integrating predicted survival or metastasis-related benefit with predicted risks of clinically important adverse events. This analysis will be considered exploratory and will be presented transparently with assumptions about outcome weighting.

Missing baseline covariate data will be assessed for extent and patterns. If missingness is limited, complete-case analyses may be performed; otherwise, multiple imputation using chained equations will be used under a missing-at-random assumption. Outcomes and treatment assignment will be included in imputation models where appropriate. Sensitivity analyses will compare complete-case and imputed results."
  ["project_software_used"]=>
  array(4) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(7) "rstudio"
      ["label"]=>
      string(7) "RStudio"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(5) "stata"
      ["label"]=>
      string(5) "STATA"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(11) "open_office"
      ["label"]=>
      string(11) "Open Office"
    }
  }
  ["project_timeline"]=>
  string(1211) "The proposed project is expected to be completed within 6 months of data access approval and execution of the Data Use Agreement.

Month 1: Project initiation, data access setup within the YODA secure data environment, review of data documentation, finalization of the statistical analysis plan, and preparation of analytic datasets.

Month 2: Data cleaning, variable definition, assessment of missing data, descriptive analyses, and preliminary Kaplan-Meier and adverse event summaries.

Month 3: Primary survival analyses, multivariable Cox models, adverse event models, and initial heterogeneous treatment-effect analyses.

Month 4: Advanced prediction modeling, internal validation using bootstrapping or cross-validation, calibration and discrimination assessment, decision curve analysis, and exploratory net clinical benefit analyses.

Month 5: Sensitivity analyses, final tables and figures, interpretation of results, and drafting of the manuscript.

Month 6: Completion of manuscript draft, internal coauthor review, submission to a peer-reviewed journal or scientific meeting, and reporting of results back to the YODA Project in accordance with YODA requirements."
  ["project_dissemination_plan"]=>
  string(1461) "The primary product of this project will be a peer-reviewed manuscript reporting the development and evaluation of individualized treatment-benefit models for apalutamide in men with nonmetastatic castration-resistant prostate cancer using SPARTAN individual patient-level data. The manuscript will describe the study design, statistical methods, model performance, heterogeneous treatment-effect findings, adverse event trade-offs, and exploratory net clinical benefit results.

The target audiences include medical oncologists, urologists, radiation oncologists, clinical trialists, biostatisticians, health services researchers, guideline developers, and clinicians involved in shared decision-making for patients with advanced prostate cancer.

Potential journals for submission include JAMA Oncology, Journal of Clinical Oncology, European Urology, The Lancet Oncology, Clinical Cancer Research, Cancer, and European Journal of Cancer. Findings may also be submitted as an abstract to relevant scientific meetings, such as the American Society of Clinical Oncology Genitourinary Cancers Symposium, ASCO Annual Meeting, European Society for Medical Oncology Congress, or American Urological Association Annual Meeting.

All results will be reported in aggregate form only, without disclosure of identifiable participant information. Results will also be reported back to the YODA Project in accordance with YODA requirements and timelines."
  ["project_bibliography"]=>
  string(2442) "

Smith MR, Saad F, Chowdhury S, et al. Apalutamide Treatment and Metastasis-free Survival in Prostate Cancer. N Engl J Med. 2018;378:1408-1418. https://doi.org/10.1056/NEJMoa1715546
Small EJ, Saad F, Chowdhury S, et al. Apalutamide and overall survival in non-metastatic castration-resistant prostate cancer. Ann Oncol. 2019;30:1813-1820. https://doi.org/10.1093/annonc/mdz397
Smith MR, Saad F, Chowdhury S, et al. Apalutamide and overall survival in prostate cancer. Eur Urol. 2021;79:150-158. https://doi.org/10.1016/j.eururo.2020.08.011
Fizazi K, Shore N, Tammela TL, et al. Darolutamide in nonmetastatic, castration-resistant prostate cancer. N Engl J Med. 2019;380:1235-1246. https://doi.org/10.1056/NEJMoa1815671
Fizazi K, Shore N, Tammela TL, et al. Nonmetastatic, castration-resistant prostate cancer and survival with darolutamide. N Engl J Med. 2020;383:1040-1049. https://doi.org/10.1056/NEJMoa2001342
Hussain M, Fizazi K, Saad F, et al. Enzalutamide in men with nonmetastatic, castration-resistant prostate cancer. N Engl J Med. 2018;378:2465-2474. https://doi.org/10.1056/NEJMoa1800536
Sternberg CN, Fizazi K, Saad F, et al. Enzalutamide and survival in nonmetastatic, castration-resistant prostate cancer. N Engl J Med. 2020;382:2197-2206. https://doi.org/10.1056/NEJMoa2003892
Parker C, Castro E, Fizazi K, et al. Prostate cancer: ESMO Clinical Practice Guidelines. Ann Oncol. 2020;31:1119-1134. https://doi.org/10.1016/j.annonc.2020.06.011
Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: heterogeneous treatment effects. 2018;363:k4245. https://doi.org/10.1136/bmj.k4245
Vickers AJ, van Calster B, Steyerberg EW. Net benefit approaches to prediction models. 2016;352:i6. https://doi.org/10.1136/bmj.i6

"
  ["project_suppl_material"]=>
  bool(false)
  ["project_coi"]=>
  array(1) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(19494)
        ["id"]=>
        int(19494)
        ["title"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_9f35uG2oZxaGEq9.pdf"
        ["filename"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_9f35uG2oZxaGEq9.pdf"
        ["filesize"]=>
        int(37925)
        ["url"]=>
        string(89) "https://yoda.yale.edu/wp-content/uploads/2026/06/SV_57KskaKADT3U9Aq-R_9f35uG2oZxaGEq9.pdf"
        ["link"]=>
        string(86) "https://yoda.yale.edu/data-request/2026-0520/sv_57kskakadt3u9aq-r_9f35ug2ozxageq9-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2415"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(40) "sv_57kskakadt3u9aq-r_9f35ug2ozxageq9-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(19493)
        ["date"]=>
        string(19) "2026-06-18 15:31:19"
        ["modified"]=>
        string(19) "2026-06-18 15:31:21"
        ["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"
      }
    }
  }
  ["data_use_agreement_training"]=>
  bool(true)
  ["human_research_protection_training"]=>
  bool(true)
  ["certification"]=>
  bool(true)
  ["search_order"]=>
  string(1) "0"
  ["project_send_email_updates"]=>
  bool(false)
  ["project_publ_available"]=>
  bool(true)
  ["project_year_access"]=>
  string(0) ""
  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  bool(false)
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(19521)
    ["id"]=>
    int(19521)
    ["title"]=>
    string(28) "Data Request Approved Notice"
    ["filename"]=>
    string(32) "Data-Request-Approved-Notice.pdf"
    ["filesize"]=>
    int(195663)
    ["url"]=>
    string(81) "https://yoda.yale.edu/wp-content/uploads/2026/05/Data-Request-Approved-Notice.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2026-0340/data-request-approved-notice-71/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-71"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(19263)
    ["date"]=>
    string(19) "2026-06-25 16:08:02"
    ["modified"]=>
    string(19) "2026-06-25 16:08:02"
    ["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_review_link"]=>
  bool(false)
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(0) {
}


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


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