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)#4110 (24) {
      ["ID"]=>
      int(1846)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2019-12-12 12:49:00"
      ["post_date_gmt"]=>
      string(19) "2019-12-12 12:49:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(225) "NCT02252172 - A Phase 3 Study Comparing Daratumumab, Lenalidomide, and Dexamethasone (DRd) vs Lenalidomide and Dexamethasone (Rd) in Subjects With Previously Untreated Multiple Myeloma Who Are Ineligible for High Dose Therapy"
      ["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(192) "nct02252172-a-phase-3-study-comparing-daratumumab-lenalidomide-and-dexamethasone-drd-vs-lenalidomide-and-dexamethasone-rd-in-subjects-with-previously-untreated-multiple-myeloma-who-are-ineligi"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-10-28 13:18:09"
      ["post_modified_gmt"]=>
      string(19) "2025-10-28 17:18:09"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(241) "https://dev-yoda.pantheonsite.io/clinical-trial/nct02252172-a-phase-3-study-comparing-daratumumab-lenalidomide-and-dexamethasone-drd-vs-lenalidomide-and-dexamethasone-rd-in-subjects-with-previously-untreated-multiple-myeloma-who-are-ineligi/"
      ["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(111) "Precision Geriatric Oncology: Estimating the Heterogeneous Treatment Effect in Multiple Myeloma in Older Adults"
  ["project_narrative_summary"]=>
  string(627) "The study aims to improve treatment for older adults with Multiple Myeloma, a type of blood cancer. It uses data from a major clinical trial to understand how different patients respond to a combination of drugs. By employing advanced machine learning techniques, the research seeks to personalize treatment plans based on individual health profiles and preferences. This approach could lead to better outcomes by tailoring therapies to each patient's needs, balancing survival and quality of life. The findings could significantly enhance healthcare for the elderly, offering more effective and personalized cancer treatments."
  ["project_learn_source"]=>
  string(5) "other"
  ["project_learn_source_exp"]=>
  string(13) "Google Search"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Andre"
    ["last_name"]=>
    string(8) "Williams"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(27) "Florida Atlantic University"
    ["email"]=>
    string(19) "wandre719@gmail.com"
    ["state_or_province"]=>
    string(2) "FL"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
  bool(false)
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["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(1557) "Background: Multiple Myeloma (MM) is a complex cancer predominantly affecting older adults. The introduction of novel therapies has improved outcomes, but the heterogeneity in the geriatric population poses challenges in treatment optimization.



Objective: To estimate the Heterogeneous Treatment Effect (HTE) of Daratumumab, Lenalidomide, and Dexamethasone (D-Rd) versus Lenalidomide and Dexamethasone (Rd) in older adults with MM, focusing on personalized treatment strategies.



Study Design: This study utilizes a Phase 3, randomized, open-label, active-controlled trial design, leveraging data from the MAIA trial (NCT02252172) accessed via the YODA Project.



Participants: The study targets patients aged 70 and older who are ineligible for autologous stem-cell transplantation, with an estimated sample size of approximately 582.



Primary and Secondary Outcome Measure(s): The primary outcomes are Overall Survival (OS) and Progression-Free Survival (PFS). Secondary outcomes include Quality of Life (QoL) measures, specifically the EuroQol-5D (EQ-5D).



Statistical Analysis: Bayesian Additive Regression Trees (BART) will be used to estimate the Conditional Average Treatment Effect (CATE) for survival and QoL outcomes. The Net Clinical Benefit (NCB) score will synthesize these estimates, incorporating patient preference weights. Policy Trees will be developed to generate personalized treatment rules, with sensitivity analyses conducted via bootstrapping to assess stability."
  ["project_brief_bg"]=>
  string(2654) "Background:



The research project focuses on improving treatment strategies for older adults with Multiple Myeloma (MM), a complex and incurable cancer that predominantly affects this demographic. With a median age at diagnosis of 69 years, MM presents unique challenges due to the heterogeneity in the geriatric population.(Abdallah & Kumar, 2024) The introduction of novel therapies, such as proteasome inhibitors, immunomodulatory drugs, and anti-CD38 monoclonal antibodies, has significantly improved survival outcomes(Larocca et al., 2023). However, these advancements have also highlighted the variability in treatment responses among older adults, who may range from "fit" to "ultra-frail(Palumbo et al., 2015)."



Significance:



The project aims to address the limitations of current clinical trial frameworks, which often rely on "Average Treatment Effects" (ATE) that do not account for individual variability. By estimating the Heterogeneous Treatment Effect (HTE) at the personal level, the research seeks to move beyond averages and develop personalized treatment strategies. This is particularly important for older adults, where the balance between survival and quality of life is crucial(Kent et al., 2018). The study leverages data from the MAIA trial, focusing on the combination of Daratumumab, Lenalidomide, and Dexamethasone (D-Rd) versus Lenalidomide and Dexamethasone (Rd) in patients aged 70 and older(Kumar et al., 2022).



Enhancing Scientific and Medical Knowledge:



The project employs advanced machine learning techniques, specifically Bayesian Additive Regression Trees (BART), to estimate the Conditional Average Treatment Effect (CATE) for survival and quality-of-life outcomes(Li et al., 2023). The creation of a Net Clinical Benefit (NCB) score that incorporates patient preferences enables the development of personalized treatment rules through Policy Trees. This approach transforms complex HTE estimates into actionable clinical guidelines, providing a dynamic decision-support system that aligns with individual patient priorities(Bodory et al., 2024).



The information gained from this work will enhance generalizable scientific and medical knowledge by providing a robust framework for precision oncology. It will inform treatment strategies for the fastest-growing MM demographic, directly improving healthcare value and patient outcomes. The methodology developed can be validated in external datasets, ensuring its applicability across different clinical settings and contributing to the broader field of geriatric oncology."
  ["project_specific_aims"]=>
  string(1561) "Specific Aims of the Project:



The project aims to enhance treatment strategies for older adults with Multiple Myeloma (MM) by estimating the Heterogeneous Treatment Effect (HTE) of the combination therapy Daratumumab, Lenalidomide, and Dexamethasone (D-Rd) compared to Lenalidomide and Dexamethasone (Rd) alone. The study focuses on patients aged 70 and older who are ineligible for autologous stem cell transplantation.



Study Objectives:



1. Estimate HTE: Utilize Bayesian Additive Regression Trees (BART) to estimate the Conditional Average Treatment Effect (CATE) for survival outcomes (Overall Survival and Progression-Free Survival) and quality-of-life measures (EQ-5D).



2. Develop Personalized Treatment Rules: Create Policy Trees based on the Net Clinical Benefit (NCB) score, which integrates patient preferences to generate personalized treatment guidelines.



Specific Hypotheses:



1. Policy Null Hypothesis (H0): The optimal policy assigns the same treatment (D-Rd or Rd) to all patients, regardless of baseline covariates and patient preferences, providing no significant net benefit over a uniform treatment approach.



2. Policy Heterogeneity Hypothesis (H1): The optimal policy identifies specific subgroups of patients who benefit differently from D-Rd versus Rd, resulting in a statistically significant positive net benefit. This confirms the existence of subgroups for whom the addition of Daratumumab is either futile or maximally beneficial."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
  }
  ["project_purposes"]=>
  array(2) {
    [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(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(950) "Inclusion Criteria:



1. Age: Patients aged 70 years and older at the time of randomization.

2. Diagnosis: Patients with newly diagnosed Multiple Myeloma (MM) who are ineligible for autologous stem-cell transplantation.

3. Trial Participation: Participants from the MAIA trial (NCT02252172), which is a Phase 3, randomized, open-label, active-controlled trial.



Exclusion Criteria:



- None 



Data Sources:



- The primary data source is the MAIA trial, accessed through the Yale University Open Data Access (YODA) Project.



This study focuses on leveraging high-quality randomized data from the MAIA trial to estimate the Heterogeneous Treatment Effect (HTE) of the D-Rd regimen compared to Rd alone in older adults with MM. The analysis aims to develop personalized treatment strategies by considering individual patient characteristics and preferences."
  ["project_main_outcome_measure"]=>
  string(1343) "Primary Outcome Measures:



1. Overall Survival (OS): This is defined as the time from randomization to death from any cause. It is a critical endpoint for assessing the efficacy of the treatment regimens in prolonging life in patients with Multiple Myeloma.



2. Progression-Free Survival (PFS): This measures the time from randomization until disease progression or death, whichever occurs first. It serves as an indicator of the treatment's ability to delay disease advancement.



Secondary Outcome Measures:



1. Quality of Life (QoL) - EuroQol-5D (EQ-5D): This is a patient-reported outcome measure that evaluates the quality of life across five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. It provides insights into the impact of treatment on patients' daily living and well-being.



These outcomes are designed to capture both the clinical efficacy and the patient-centered impact of the treatment regimens, allowing for a comprehensive assessment of their benefits and drawbacks. The study will employ Bayesian Additive Regression Trees (BART) to estimate the Conditional Average Treatment Effect (CATE) for these outcomes, integrating them into a Net Clinical Benefit (NCB) score to guide personalized treatment decisions."
  ["project_main_predictor_indep"]=>
  string(712) "Main Independent Variable(s):



1. Treatment Arm (D-Rd vs. Rd): The primary independent variable in this study is the treatment regimen assigned to patients. The experimental group receives Daratumumab, Lenalidomide, and Dexamethasone (D-Rd), while the control group receives Lenalidomide and Dexamethasone (Rd). This variable is crucial for assessing the independent effect of adding Daratumumab to the standard regimen on both primary and secondary outcomes.



This independent variable is central to the study's aim of estimating the Heterogeneous Treatment Effect (HTE) and developing personalized treatment rules. The treatment arm directly assesses the effect of the D-Rd regimen."
  ["project_other_variables_interest"]=>
  string(1807) "Other Variables of Interest:



1. Disease Biology:

   - ISS Stage (I, II, III): Categorized based on the International Staging System for Multiple Myeloma, which is a standard prognostic risk factor.

   - M-protein Isotype (IgG, IgA, Light Chain): Defined by the type of monoclonal protein produced by the myeloma cells.



2. Cytogenetics (FISH):

   - Presence of t(4;14), t(14;16), del(17p), gain(1q21), del(1p): These are key cytogenetic abnormalities that drive heterogeneity in treatment response and prognosis. They will be categorized as present or absent.



3. Frailty & Function:

   - ECOG Performance Status (0, 1, 2): A measure of the patient's level of functioning and ability to care for themselves, categorized by standard ECOG criteria.

   - Charlson Comorbidity Index: Derived from medical history to assess the burden of comorbid conditions.

   - Renal Function (CrCl): Measured by creatinine clearance, which influences Lenalidomide toxicity.

   - Baseline Neuropathy: Presence or absence of neuropathy at baseline.



4. Additional variables:

   - Age

   - Gender

   - Serum Albumin

   - Hemoglobin Severity

   - Montreal Cognitive Assessment (MOCA) score



These variables will be used to characterize the study sample and for multivariable risk adjustment in the analysis. They serve as confounders in the estimation of the Conditional Average Treatment Effect (CATE) and help illuminate heterogeneity in treatment outcomes. By incorporating these variables, the study aims to provide a comprehensive analysis that accounts for the complex interplay of biological, genetic, and functional factors in older adults with Multiple Myeloma."
  ["project_stat_analysis_plan"]=>
  string(2708) "To analyze the clinical trial data from the MAIA trial, we will employ a comprehensive approach that includes descriptive, bivariate, multivariable, and advanced analyses. The detailed plan is presented below:



1. Descriptive Analysis:



- Demographics and Baseline Characteristics: We will begin by summarizing the demographic and baseline clinical characteristics of the study population. This includes age, gender, disease stage, cytogenetic profiles, and frailty indicators. Descriptive statistics such as means, medians, standard deviations, and proportions will be used to provide an overview of the study sample.



2. Bivariate Analysis:



- Comparative Analysis: We will conduct bivariate analyses to compare baseline characteristics and outcomes between the treatment arms (D-Rd vs. Rd). Chi-square tests for categorical variables and t-tests or Mann-Whitney U tests for continuous variables will be used to assess differences.



3. Advanced Analyses:



- Bayesian Additive Regression Trees (BART): We will use BART to estimate the Conditional Average Treatment Effect (CATE) for both survival and quality-of-life outcomes. BART is a nonparametric method that excels at modeling complex, nonlinear relationships and interactions, providing robust estimates of treatment effects.



- Competing Risks Analysis: For survival outcomes subject to competing risks, we will use BART to estimate cause-specific hazards or cumulative incidence functions, ensuring accurate modeling of these events.



- Net Clinical Benefit (NCB) Score: We will compute the NCB score to synthesize CATE estimates across different domains, balancing survival and quality-of-life outcomes. This score will be used as the optimization target for generating personalized treatment rules.



- Policy Trees: Using the NCB score, we will develop policy trees to generate personalized treatment rules. Policy trees provide interpretable, rule-based outputs that maximize expected outcomes based on patient covariates.



5. Sensitivity Analysis:



- Bootstrapping: To assess the stability of the policy trees, we will perform sensitivity analyses using bootstrapping. This involves resampling the data with replacement and applying the policy tree algorithm to these resamples. Consistent results across resamples will confirm the stability of the policy trees.



This comprehensive analytical approach will allow us to assess treatment effects rigorously, account for patient heterogeneity, and develop personalized treatment strategies for older adults with Multiple Myeloma."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(1237) "Estimated Key Milestone Dates for the Proposed Study:



- Anticipated Project Start Date: January 2026  

  This date assumes prompt approval of the data request and completion of the Data Use Agreement with the YODA Project.



- Analysis Completion Date: July 2026  

  This allows for six months of data analysis, including descriptive, bivariate, and advanced analyses using BART and Policy Trees.



- Date Manuscript Drafted and First Submitted for Publication: September 2026  

  Following data analysis, two months are allocated to drafting the manuscript, incorporating co-authors' feedback, and preparing for submission.



- Date Results Reported Back to the YODA Project: October 2026  

  This date ensures that results are reported back to the YODA Project within the 12-month data access period, allowing for any necessary revisions or additional analyses.



These dates are tentative and may be adjusted based on the study's progress and any unforeseen challenges. The timeline is designed to ensure compliance with the YODA Project's data access period while allowing sufficient time for thorough analysis and manuscript preparation."
  ["project_dissemination_plan"]=>
  string(1719) "Dissemination Plan



Anticipated Products:



1. Manuscript: The primary product of this research will be a comprehensive manuscript detailing the study's methodology, findings, and implications for clinical practice. The manuscript will focus on estimating Heterogeneous Treatment Effects (HTE) using Bayesian Additive Regression Trees (BART) and on developing personalized treatment rules through Policy Trees. It will highlight the creation and application of the Net Clinical Benefit (NCB) score, emphasizing its role in optimizing treatment strategies for older adults with Multiple Myeloma.



2. Conference Presentation: The study findings will be presented at a major scientific conference. This presentation will engage clinicians, researchers, and health research data scientists by providing insights into the application of machine learning techniques in geriatric oncology.



Target Audience:



- Clinicians: Oncologists and geriatric specialists who are involved in the treatment of Multiple Myeloma, particularly those interested in personalized medicine approaches.

- Researchers: Academics and scientists focused on oncology, machine learning, and health outcomes research.

- Health Research Data Scientists: Professionals working at the intersection of data science and healthcare, particularly those interested in the application of advanced analytics to clinical trial data.



Potential Journals for Submission:



1. AI Medicine

2. JCO Clinical Cancer Informatics



By targeting these audiences and journals, the dissemination plan aims to maximize the impact of the research."
  ["project_bibliography"]=>
  string(3974) "
Abdallah, N., & Kumar, S. (2024). Up-Front Treatment of Elderly (Age ≥75 Years) and Frail Patients With Multiple Myeloma [Review of Up-Front Treatment of Elderly (Age ≥75 Years) and Frail Patients With Multiple Myeloma]. Journal of the National Comprehensive Cancer Network, 22(9). National Comprehensive Cancer. https://doi.org/10.6004/jnccn.2024.7039
 
Bodory, H., Mascolo, F., & Lechner, M. (2024). Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignment. Algorithms, 17(7), 318. https://doi.org/10.3390/a17070318
 
Kent, D. M., Steyerberg, E. W., & Klaveren, D. van. (2018). Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects [Review of Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects]. BMJ, 363. https://doi.org/10.1136/bmj.k4245
 
Kumar, S., Moreau, P., Bahlis, N. J., Façon, T., Plesner, T., Orlowski, R. Z., Basu, S., Nahi, H., Hulin, C., Quach, H., Goldschmidt, H., O’Dwyer, M., Perrot, A., Venner, C. P., Weisel, K., Raje, N., Tiab, M., Macro, M., Frenzel, L., … Usmani, S. Z. (2022). Daratumumab Plus Lenalidomide and Dexamethasone (D-Rd) Versus Lenalidomide and Dexamethasone (Rd) Alone in Transplant-Ineligible Patients with Newly Diagnosed Multiple Myeloma (NDMM): Updated Analysis of the Phase 3 Maia Study. Blood, 140, 10150. https://doi.org/10.1182/blood-2022-163335
 
Larocca, A., Cani, L., Bertuglia, G., Bruno, B., & Bringhen, S. (2023). New Strategies for the Treatment of Older Myeloma Patients [Review of New Strategies for the Treatment of Older Myeloma Patients]. Cancers, 15(10), 2693. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/cancers15102693
 
Li, X., Logan, B. R., Hossain, S. M. F., & Moodie, E. E. M. (2023). Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes. Lifetime Data Analysis, 30(1), 181. https://doi.org/10.1007/s10985-023-09605-8
 
Palumbo, A., Bringhen, S., Mateos, M., Larocca, A., Façon, T., Kumar, S., Offidani, M., McCarthy, P. L., Evangelista, A., Lonial, S., Zweegman, S., Musto, P., Terpos, E., Belch, A., Hájek, R., Ludwig, H., Stewart, A. K., Moreau, P., Anderson, K. C., … Rajkumar, S. V. (2015). Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report. Blood, 125(13), 2068. https://doi.org/10.1182/blood-2014-12-615187
"
  ["project_suppl_material"]=>
  bool(false)
  ["project_coi"]=>
  array(1) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18461)
        ["id"]=>
        int(18461)
        ["title"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_3cdWDapXGK1szaF.pdf"
        ["filename"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_3cdWDapXGK1szaF.pdf"
        ["filesize"]=>
        int(18286)
        ["url"]=>
        string(89) "https://yoda.yale.edu/wp-content/uploads/2025/12/SV_57KskaKADT3U9Aq-R_3cdWDapXGK1szaF.pdf"
        ["link"]=>
        string(86) "https://yoda.yale.edu/data-request/2025-0888/sv_57kskakadt3u9aq-r_3cdwdapxgk1szaf-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1806"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(40) "sv_57kskakadt3u9aq-r_3cdwdapxgk1szaf-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18453)
        ["date"]=>
        string(19) "2025-12-16 17:13:11"
        ["modified"]=>
        string(19) "2025-12-16 17:13:13"
        ["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(18476)
    ["id"]=>
    int(18476)
    ["title"]=>
    string(28) "Data Request Approved Notice"
    ["filename"]=>
    string(34) "Data-Request-Approved-Notice-2.pdf"
    ["filesize"]=>
    int(195663)
    ["url"]=>
    string(83) "https://yoda.yale.edu/wp-content/uploads/2025/10/Data-Request-Approved-Notice-2.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2025-0728/data-request-approved-notice-65/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-65"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18117)
    ["date"]=>
    string(19) "2025-12-19 17:34:24"
    ["modified"]=>
    string(19) "2025-12-19 17:34:24"
    ["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"
}