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      string(257) "NCT01715285 - A Randomized, Double-blind, Comparative Study of Abiraterone Acetate Plus Low-Dose Prednisone Plus Androgen Deprivation Therapy (ADT) Versus ADT Alone in Newly Diagnosed Subjects With High-Risk, Metastatic Hormone-naive Prostate Cancer (mHNPC)"
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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(90) "Impact of Age as a Continuous Variable on Outcome in Metastatic Hormone-Sensitive Patients"
  ["project_narrative_summary"]=>
  string(855) "Prostate cancer is a disease that mainly affects older men, and new treatments have greatly improved survival for patients with metastatic disease sensitive to hormone therapy. However, older men are often underrepresented in clinical trials, and most studies divide patients into broad age groups (for example, under or over 70 years old). This approach can miss important differences in how age affects outcomes, side effects, and quality of life.

This research will combine information from several randomized clinical trials and  a prospective real-world study. Using data from thousands of men, we will study age as a continuous factor, rather than grouping by arbitrary cutoffs. This will help us understand whether increasing age changes how well treatments work, how likely side effects are, and how quality of life evolves during therapy. "
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "David"
    ["last_name"]=>
    string(7) "Lorente"
    ["degree"]=>
    string(7) "MD, PhD"
    ["primary_affiliation"]=>
    string(34) "Instituto Valenciano de Oncología"
    ["email"]=>
    string(24) "lorente.davest@gmail.com"
    ["state_or_province"]=>
    string(8) "Valencia"
    ["country"]=>
    string(5) "Spain"
  }
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  ["project_ext_grants"]=>
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    ["value"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1686) "Background:

Age is a key clinical factor in metastatic hormone-sensitive prostate cancer (mHSPC) but is typically analyzed categorically, with arbitrary cut-offs, without accounting for potential non-linear relationships. Evaluating age as a continuous variable may provide a more precise understanding of its association with outcomes, toxicity, and quality of life.

Objective:

To assess the association between age (continuous) and outcomes in mHSPC, including its relationship with baseline characteristics, toxicity, quality of life, and modification of treatment effect.

Study Design:

Retrospective, pooled individual patient data analysis using data from the TITAN and LATITUDE trials. Data will be combined with those from a prospective real world cohort and the CHAARTED trial dataset. 

Participants:

Men with mHSPC receiving ADT with or without systemic intensification (apalutamide, abiraterone, or docetaxel).

Primary and Secondary Outcome Measure(s):

Primary outcomes: overall survival and progression-free survival. Secondary outcomes: associations of continuous age with baseline characteristics (PSA, LDH, ALP, albumin, prior local therapy, disease volume), grade ≥3 adverse events, treatment discontinuation, and quality-of-life scores.

Statistical Analysis:

Age will be modeled as a continuous predictor using restricted cubic splines. Cox models will estimate associations with survival outcomes; logistic and linear models will assess toxicity and quality of life. Interaction terms will explore treatment-by-age effects, with adjustment for disease volume and baseline covariates

"
  ["project_brief_bg"]=>
  string(3041) "Prostate cancer predominantly affects older men, and age is a key consideration in treatment selection and outcomes. However, most clinical and observational studies evaluate age as a categorical variable—often dichotomized at arbitrary thresholds such as 65, 70 or 75 years. This approach leads to information loss, reduced statistical power, and the potential masking of non-linear associations between age and treatment outcomes(1). Modern analytic approaches allow age to be modeled as a continuous variable, enabling a more nuanced understanding of how age influences prognosis, treatment efficacy, toxicity, and quality of life in metastatic hormone-sensitive prostate cancer (mHSPC).



Pivotal phase III trials such as TITAN(2) and LATITUDE(3) established the benefit of androgen-receptor pathway inhibitors such as Apalutamide and Abiraterone in mHSPC, while CHAARTED demonstrated improved survival with docetaxel + ADT(4). All of these trials were compared against a control ADT-only arm. Real world prospective cohort studies such as the CAPTURE study(5), whose primary endpoint was to evaluate the impact of HRR genomic alterations on prognosis, have confirmed the antitumor activity of these agents in the metastatic hormone-sensitive stage of the disease. 



Subgroup analyses by age of the clinical trials have used categorized age based on the age distribution of the dataset(6) rather than on a clinical or biological rationale. Categorizing age into distinct subgroups has prevented the detection of non-linear effects, and limited the ability to detect interaction effects. 

Pooling individual patient-level data from these four datasets—TITAN, LATITUDE, CHAARTED, and CAPTURE—will allow rigorous evaluation of the relationship between age (as a continuous variable) and key outcomes (overall survival, progression-free survival, toxicity, and quality of life) across distinct therapeutic contexts and clinical settings. Additionally, by assessing treatment-by-age interactions, this research will clarify whether the efficacy of systemic intensification (androgen-receptor inhibitors or chemotherapy) varies with age after accounting for disease volume and baseline characteristics.



The results will enhance generalizable scientific and clinical knowledge by:

1.	Providing an evidence-based, continuous framework for incorporating age into prognostic and predictive models for mHSPC.

2.	Identifying whether older age independently influences treatment benefit or risk, beyond comorbidity and disease burden.

3.	Informing individualized treatment decision-making and shared discussions between clinicians and patients.

4.	Comparing trial and real-world cohorts to assess external validity and representativeness of pivotal studies.

Findings will be disseminated through peer-reviewed publications and presentations to inform clinical guidelines and future trial designs that better represent older adults with prostate cancer.

"
  ["project_specific_aims"]=>
  string(1528) "Specific Aims:

The goal of this project is to evaluate the association between age (as a continuous variable) and clinical outcomes in men with metastatic hormone-sensitive prostate cancer (mHSPC), integrating data from the TITAN, LATITUDE, CHAARTED, and CAPTURE studies.



Primary Objective:

• To assess the relationship between age (continuous) and overall survival and progression-free survival in patients with mHSPC.



Secondary Objectives:

1. To evaluate associations between age (continuous) and baseline characteristics, including PSA, LDH, ALP, albumin, prior local therapy, and disease volume.

2. To assess the relationship between age and treatment toxicity (grade ≥3 adverse events, discontinuation rates) and quality of life (FACT-P, EQ-5D, or equivalent measures).

3. To explore the interaction between age and treatment effect (treatment-by-age interaction) for survival outcomes, in models with and without adjustment for disease volume.

4. To compare age-outcome relationships between clinical trial populations and the real-world CAPTURE cohort to assess generalizability.



Hypotheses:

1.	Age, modeled continuously, is independently associated with survival, toxicity, and quality of life in mHSPC.

2.	The effect of systemic treatment (AR inhibitors, chemotherapy) on survival varies non-linearly with age.

3.	Patterns of association between age and outcome differ between trial and real-world settings."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(3) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [2]=>
    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(1812) "For the TITAN and LATITUDE (requested through the YODA Project), we will include all participants who meet the original trial inclusion criteria for metastatic hormone-sensitive prostate cancer (mHSPC) and for whom baseline age and outcome data are available.

Inclusion criteria:

•	Histologically confirmed adenocarcinoma of the prostate.

•	Evidence of metastatic disease at diagnosis or relapse (mHSPC).

•	Initiation of androgen deprivation therapy (ADT) with or without systemic intensification (apalutamide, abiraterone, or docetaxel).

•	Availability of baseline age and relevant covariates (PSA, LDH, ALP, albumin, prior local therapy, disease volume).

•	Participation in the TITAN, LATITUDE, or CHAARTED trials.

Exclusion criteria:

•	Missing or incomplete data on age or primary outcomes (overall survival or progression-free survival).

•	Enrollment in non-mHSPC or castration-resistant cohorts (if any).

•	Duplicate or withdrawn cases (if applicable per dataset).

Additional Data Sources:

Data from the CAPTURE study, a prospective, real-world cohort of patients with mHSPC treated with ADT ± systemic intensification. CAPTURE data will be accessed through the study’s academic coordinating center. Data from the CHAARTED study will be requested from the NCTN/NCORP Data Archive (National Cancer Institute). 

Data Integration Plan:

Individual patient data (IPD) from the YODA trials, CAPTURE and CHAARTED will be pooled for harmonized analyses using a secure institutional research computing environment. Statistical analyses will be conducted on the YODA platform, with harmonized variables across datasets. No summary-level aggregation only—full IPD analysis will be performed.

"
  ["project_main_outcome_measure"]=>
  string(1061) "Primary Outcome Measures:

1.	Overall Survival (OS): Time from start of therapy  to death from any cause. Patients alive at the time of data cutoff will be censored at the last known date alive.

2.	Radiographic Progression-Free Survival (rPFS): Time from start of therapy to radiographic progression  or death, whichever occurs first.

 

Secondary Outcome Measures:

1.	Toxicity: Incidence of grade ≥3 adverse events (per CTCAE criteria) and treatment discontinuations due to toxicity.

2.	Quality of Life (QoL): Longitudinal scores from validated instruments (FACT-P, EQ-5D, or corresponding study-specific scales), focusing on baseline, early (3–6 months), and longitudinal change.

 

Changes to Outcome Measures:

No changes to the primary or secondary outcomes are anticipated between this proposal and the final publication. Any additional exploratory endpoints (e.g., functional form of age using splines, stratified subgroup analyses) will be clearly reported as hypothesis-generating.

"
  ["project_main_predictor_indep"]=>
  string(1343) "The main independent variable in this study is patient age at baseline, defined as the age (in years) at the time of start of therapy. 

Age will be analyzed as a continuous variable to preserve the full range of information and to avoid the loss of power and interpretability associated with arbitrary categorical cutoffs (e.g., <65 vs ≥65 years). This continuous approach will enable the evaluation of both linear and non-linear relationships between age and clinical outcomes, toxicity, and quality of life.

To model potential non-linear associations, restricted cubic spline functions will be applied to age in all multivariable regression models (Cox proportional hazards for survival outcomes; logistic and linear models for toxicity and QoL outcomes, respectively). The number and placement of spline knots will follow standard recommendations (typically 3–5 knots at percentiles of the age distribution).

Age will also be included as an interaction term with treatment arm (treatment × age) to evaluate whether treatment effects (on OS, PFS, toxicity, or QoL) vary across the age continuum.

For descriptive purposes, summary statistics will also present age by quantiles (e.g., quartiles) to facilitate interpretability, but no categorical age groupings will be used for inferential analyses.

"
  ["project_other_variables_interest"]=>
  string(2164) "Other Variables of Interest:

Additional variables will be used to characterize the study population and for covariate adjustment in multivariable models assessing the association between age and outcomes.

Baseline Disease Characteristics:

•	Prostate-Specific Antigen (PSA): Continuous variable (ng/mL) at baseline; log-transformed where appropriate to address skewness.

•	Lactate Dehydrogenase (LDH): Continuous variable (U/L) at baseline; categorized as normal vs above upper limit of normal (ULN) if study-specific reference ranges are available.

•	Alkaline Phosphatase (ALP): Continuous variable (U/L); also evaluated as normal vs above ULN.

•	Albumin: Continuous variable (g/dL); analyzed both continuously and as <35 vs ≥35 g/L, if appropriate.

•	Disease Volume: Binary variable (high vs low), defined per each trial’s criteria (e.g., CHAARTED criteria—visceral metastases and/or ≥4 bone lesions with ≥1 beyond the axial skeleton).

•	Number of bone metastases by bone scan, presence of visceral metastases (yes/no). 

•	Prior Local Therapy: Binary variable (yes/no), including radical prostatectomy and/or radiotherapy before initiation of ADT.

•	Performance Status: ECOG performance status (0–1 vs ≥2).

Treatment Variables:

•	Treatment Arm: categorical variable indicating systemic therapy received in combination with ADT (apalutamide, abiraterone, docetaxel, abiraterone + docetaxel or ADT alone).

•	Treatment Discontinuation: Binary variable (yes/no) indicating early cessation of assigned therapy due to toxicity or other causes.

Outcome-Related Covariates:

•	Adverse Events: Highest grade per CTCAE version used in each trial; grade ≥3 events analyzed as binary outcomes.

•	Quality of Life: Continuous measures from validated instruments (FACT-P, EQ-5D, or equivalent), analyzed as change from baseline and longitudinally using mixed-effects models.



All variables will be harmonized across studies following standardized definitions to ensure comparability in pooled analyses.

"
  ["project_stat_analysis_plan"]=>
  string(4121) "All analyses will be performed on de-identified, individual patient data (IPD) from the TITAN, LATITUDE and CHAARTED clinical trials and the CAPTURE prospective real-world cohort. Data from all sources will be harmonized prior to analysis, using consistent variable definitions and units. Analyses will be conducted within a secure institutional research computing environment using R (version ≥4.3).



1. Descriptive Analyses

Baseline demographic and clinical characteristics will be summarized overall and by study, treatment arm, and quartiles of age for interpretability. Continuous variables will be described using medians (IQR) or means (SD), and categorical variables as frequencies (%). Between-group differences will be tested using t-tests, Wilcoxon rank-sum, chi-square, or Fisher’s exact tests as appropriate. Missing data will be summarized, and sensitivity analyses will compare complete-case and multiply imputed datasets (multiple imputation by chained equations).



2. Primary Analyses

The primary independent variable is age at baseline, modeled as a continuous variable using restricted cubic splines (3–5 knots at standard percentiles) to allow for non-linear associations.

a. Survival Outcomes (Primary Endpoints):

•	Overall Survival (OS) and Progression-Free Survival (PFS / rPFS) will be analyzed using Cox proportional hazards regression.

•	Unadjusted models will estimate crude hazard ratios (HRs) for age.

•	Multivariable models will adjust for predefined covariates: ECOG performance status, disease volume, PSA, ALP, LDH, albumin, prior local therapy, and treatment arm.

•	The proportional hazards assumption will be evaluated using Schoenfeld residuals.

•	Restricted cubic spline plots will display adjusted HRs across the age continuum.

b. Interaction Analyses:

Treatment-by-age interaction terms will assess whether the effect of systemic intensification (apalutamide, abiraterone, or docetaxel) differs by age. Models will be estimated both with and without adjustment for disease volume. Significant interactions (p < 0.05) will be explored graphically using marginal effect plots.



3. Secondary Analyses

•	Toxicity Outcomes: Logistic regression models will estimate odds ratios for grade ≥ 3 adverse events and treatment discontinuation as a function of continuous age.

•	Quality of Life (QoL): Linear mixed-effects models with random intercepts for subjects will assess longitudinal change in QoL scores (FACT-P, EQ-5D). Fixed effects will include age (continuous), time, treatment arm, and age × time interaction terms.

•	Baseline Associations: Linear or logistic regression will relate age to baseline characteristics (PSA, LDH, ALP, albumin, disease volume, prior local therapy).



4. Real-World vs Trial Comparison

Analyses will be performed separately within each dataset and pooled via one-stage IPD meta-analysis (with study as a random effect). Heterogeneity across studies will be quantified using τ² and I² statistics. Comparative analyses between trial and CAPTURE cohorts will test for effect consistency (interaction p values).



5. Sensitivity and Subgroup Analyses

•	Alternative age parameterizations 

•	Models excluding patients with missing baseline variables.

•	Stratified analyses by disease volume, treatment class, and ECOG status.

•	Propensity-score weighting to account for baseline differences between trials and the real-world cohort.



6. Reporting

Results will be presented as adjusted HRs or ORs with 95 % confidence intervals. Two-sided p < 0.05 will define statistical significance. Graphical presentations will include spline curves, Kaplan–Meier plots by age quantiles, and forest plots for subgroup effects.

All analyses will follow reproducible-research principles, with analytic code archived and results transparently reported to ensure comparability with the final publication.

"
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      ["label"]=>
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  ["project_timeline"]=>
  string(1252) "Project Timeline:

•	Project Start (Data Access Granted): Month 1 — Initiation of data use agreement, data transfer, and harmonization of datasets

•	Data Cleaning and Variable Harmonization: Months 1–3 — Standardize variable definitions, confirm completeness, and perform exploratory data checks.

•	Descriptive and Baseline Analyses: Months 3–6 — Summarize baseline characteristics, assess missing data patterns, and complete data imputation.

•	Primary and Secondary Analyses: Months 3–6 — Conduct multivariable Cox, logistic, and mixed-effects models; complete treatment-by-age interaction analyses.

•	Sensitivity and Real-World Comparative Analyses: Months 9–12 — Perform pooled IPD meta-analysis and cross-validation with CAPTURE data.

•	Manuscript Preparation: Months 12–15 — Draft and internally review manuscript; prepare tables, figures, and supplementary material.

•	Manuscript Submission: Month 15 — Submit first manuscript to a peer-reviewed journal (target: European Urology or Annals of Oncology).

•	Results Reporting: Month 15 — Submit summary results and key findings to the YODA Project in accordance with data-sharing requirements.

"
  ["project_dissemination_plan"]=>
  string(1334) "The results of this study will be disseminated through multiple academic and public channels to maximize scientific and clinical impact. The primary product will be a peer-reviewed manuscript detailing the association between age (as a continuous variable) and outcomes, toxicity, and quality of life in metastatic hormone-sensitive prostate cancer (mHSPC).

The target audience includes clinical researchers, oncologists, urologists, biostatisticians, and guideline-development groups involved in prostate cancer management. The initial manuscript will be submitted to a high-impact oncology or urology journal such as European Urology, Annals of Oncology, Journal of Clinical Oncology, or The Lancet Oncology.

Secondary manuscripts may address methodological aspects (continuous age modeling), comparisons between clinical trial and real-world data (CAPTURE), and treatment-by-age interactions for specific drug classes (androgen-receptor inhibitors or docetaxel).

Key findings will also be presented at major international meetings, including ASCO, ESMO, and APCCC. A concise report of study outcomes will be returned to the YODA Project, ensuring transparency and accessibility. The overall goal is to generate generalizable evidence to inform clinical decision-making and future trial design in mHSPC.

"
  ["project_bibliography"]=>
  string(1862) "

Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ [Internet]. 2006 May 6;332(7549):1080. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1458573&tool=pmcentrez&rendertype=abstract
Chi KN, Agarwal N, Bjartell A, Chung BH, Gomes AJPDS, Given R, et al. Apalutamide for metastatic, castration-sensitive prostate cancer. N Engl J Med. 2019;381(1):13–24.
Fizazi K, Tran NP, Fein L, Matsubara N, Rodriguez-Antolin A, Alekseev BY, 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 [Internet]. 2019;20(5):686–700. Available from: http://dx.doi.org/10.1016/S1470-2045(19)30082-8
Kyriakopoulos CE, Chen YH, Carducci MA, Liu G, Jarrard DF, Hahn NM, et al. Chemohormonal therapy in metastatic hormone-sensitive prostate cancer: long-term survival analysis of the randomized phase III E3805 chaarted trial. J Clin Oncol. 2018;36(11):1080–7.
Olmos D, Lorente D, Jambrina A, Tello-Velasco D, Ovejero-Sánchez M, Gonzalez-Ginel I, et al. BRCA1/2 and homologous recombination repair alterations in high- and low-volume metastatic hormone-sensitive prostate cancer: prevalence and impact on outcomes. Ann Oncol [Internet]. 2025 Jun;m(xxx). Available from: https://doi.org/10.1016/j.annonc.2025.05.534
Lage DE, Michaelson MD, Lee RJ, Greer JA, Temel JS, Sweeney CJ. Outcomes of older men receiving docetaxel for metastatic hormone-sensitive prostate cancer. Prostate Cancer Prostatic Dis. 2021;24(4):1181–8.

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    ["title"]=>
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    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2025-0704.pdf"
    ["filesize"]=>
    int(126471)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Due-Diligence-Assessment-2025-0704.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2025-0704/yoda-project-due-diligence-assessment-2025-0704/"
    ["alt"]=>
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    ["description"]=>
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    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2025-0704"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18034)
    ["date"]=>
    string(19) "2026-01-15 15:17:06"
    ["modified"]=>
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    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
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    ["type"]=>
    string(11) "application"
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    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(18599)
    ["id"]=>
    int(18599)
    ["title"]=>
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    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0704-2025-12-04.pdf"
    ["filesize"]=>
    int(135633)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Protocol-2025-0704-2025-12-04.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0704/yoda-project-protocol-2025-0704-2025-12-04/"
    ["alt"]=>
    string(0) ""
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    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0704-2025-12-04"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18034)
    ["date"]=>
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    ["modified"]=>
    string(19) "2026-01-15 15:17:40"
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    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(18600)
    ["id"]=>
    int(18600)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0704_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0704_site.pdf"
    ["filesize"]=>
    int(1331845)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Review-2025-0704_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0704/yoda-project-review-2025-0704_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
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    ["date"]=>
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  ["request_data_partner"]=>
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}
data partner
array(1) {
  [0]=>
  string(0) ""
}


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


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


products
array(0) {
}


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


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