array(41) {
  ["request_overridden_res"]=>
  string(1) "3"
  ["project_status"]=>
  string(7) "ongoing"
  ["project_assoc_trials"]=>
  array(2) {
    [0]=>
    object(WP_Post)#5548 (24) {
      ["ID"]=>
      int(1848)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2019-12-12 13:04:00"
      ["post_date_gmt"]=>
      string(19) "2019-12-12 13:04:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(256) "NCT00574132 - A Phase 3, Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Efficacy and Safety Trial of Bapineuzumab (AAB-001, ELN115727) In Patients With Mild to Moderate Alzheimer's Disease Who Are Apolipoprotein E4 Non- Carriers"
      ["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(189) "nct00574132-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-09-19 10:17:15"
      ["post_modified_gmt"]=>
      string(19) "2025-09-19 14:17:15"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(238) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00574132-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz/"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
    [1]=>
    object(WP_Post)#5549 (24) {
      ["ID"]=>
      int(1847)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2019-12-12 12:58:00"
      ["post_date_gmt"]=>
      string(19) "2019-12-12 12:58:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(251) "NCT00575055 - A Phase 3, Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Efficacy and Safety Trial of Bapineuzumab (AAB-001, ELN115727) In Patients With Mild to Moderate Alzheimer's Disease Who Are Apolipoprotein E4 Carriers"
      ["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(189) "nct00575055-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-08-26 15:24:32"
      ["post_modified_gmt"]=>
      string(19) "2025-08-26 19:24:32"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(238) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00575055-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz/"
      ["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(102) "Precision Medicine Approaches for Subgroup Identification in Bapineuzumab Alzheimer’s Disease Trials"
  ["project_narrative_summary"]=>
  string(856) "Alzheimer’s disease (AD) is a rapidly escalating public health crisis. Despite its growing burden, effective treatments remain limited, and substantial heterogeneity in disease progression and treatment response often results in modest average effects in clinical trials, potentially masking clinically meaningful benefits for specific patient subgroups. This project will leverage data from large, multi-center randomized clinical trials evaluating the efficacy and safety of bapineuzumab in patients with mild to moderately severe AD. Using rich longitudinal cognitive and clinical data, we will develop and apply rigorous subgroup identification methods—including interaction tree–based approaches integrated with mixed models for repeated measures—to identify treatment-responsive subpopulations and advance precision-medicine strategies in AD."
  ["project_learn_source"]=>
  string(9) "colleague"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(3) "Lei"
    ["last_name"]=>
    string(3) "Liu"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(34) "Washington University in St. Louis"
    ["email"]=>
    string(17) "lei.liu@wustl.edu"
    ["state_or_province"]=>
    string(2) "MO"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
  array(2) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(2) "Ke"
      ["p_pers_l_name"]=>
      string(3) "Xie"
      ["p_pers_degree"]=>
      string(2) "MS"
      ["p_pers_pr_affil"]=>
      string(34) "Washington University in St. Louis"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Zhichen"
      ["p_pers_l_name"]=>
      string(2) "Xu"
      ["p_pers_degree"]=>
      string(2) "MS"
      ["p_pers_pr_affil"]=>
      string(34) "Washington University in St. Louis"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(3) "yes"
    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1621) "Background: Alzheimer’s disease (AD) trials often show modest average effects, potentially masking meaningful benefits in specific patient subgroups. Phase 3 trials of bapineuzumab demonstrated biological target engagement but no population-level clinical benefit, suggesting substantial heterogeneity in treatment response.

Objective: To apply data-driven subgroup identification methods to uncover treatment-responsive subpopulations in bapineuzumab trials and advance precision-medicine strategies in AD research.

Study Design: Secondary analysis of randomized, double-blind, placebo-controlled trials of intravenous bapineuzumab versus placebo with 78-week longitudinal follow-up. Mixed models for repeated measures (MMRM) are integrated with nonparametric interaction tree methods to detect treatment-by-covariate interactions.

Participants: Adults aged 50–88 years with probable AD and mild-to-moderate impairment (MMSE 16–26), stratified by APOE ε4 status and randomized to bapineuzumab or placebo. The analysis population includes participants with baseline and at least one postbaseline assessment.

Primary and Secondary Outcome Measure(s): Primary outcome is longitudinal Clinical Dementia Rating–Sum of Boxes (CDR-SB). Secondary outcomes include ADAS-cog11 and Disability Assessment for Dementia (DAD).

Statistical Analysis: Outcomes are modeled using MMRM with fixed effects for treatment, visit, and their interaction, adjusting for baseline severity. Subgroup identification uses an interaction tree–MMRM framework with internal validation to ensure robustness."
  ["project_brief_bg"]=>
  string(2807) "Alzheimer’s disease (AD) is a growing public and financial healthcare crisis, affecting approximately 7.2 million individuals in the United States and projected to increase to 13.8 million by 2060 (Alzheimer’s Association, 2025). Despite extensive drug development efforts, effective therapies remain limited, and most AD phase 2/3 clinical trials have demonstrated only modest average treatment effects, constraining their perceived clinical impact. A major contributor to these disappointing results is the substantial heterogeneity in AD disease progression (Davidson et al., 2010). Patients exhibit widely varying rates of cognitive decline and clinical trajectories, which inevitably lead to heterogeneous treatment effects. Consequently, population-average analyses based on a traditional “one-size-fits-all” paradigm may obscure clinically meaningful benefits for specific patient subgroups, underscoring the need for personalized treatment strategies that account for individual characteristics such as genetic variation and disease stage.

Evidence from bapineuzumab trials exemplifies this challenge. Although bapineuzumab failed to demonstrate clinically meaningful average efficacy in phase 3 trials, multiple lines of evidence suggest substantial heterogeneity in treatment response (Salloway et al 2014). Prior analyses indicate differential effects by APOE ε4 carrier status, baseline disease severity, and amyloid burden, and a large fraction of participants exhibited minimal cognitive decline over typical trial durations, diluting average treatment effects. Moreover, bapineuzumab demonstrated biological target engagement through amyloid reduction without consistent clinical benefit at the population level. Together, these findings suggest that clinically meaningful treatment effects may be concentrated within specific subgroups that are not identifiable using conventional analyses.

Collectively, these features make bapineuzumab trial data an ideal setting for subgroup identification. In this project, we will leverage data from multiple large, multi-center randomized bapineuzumab trials to develop and apply rigorous subgroup identification methods. Specifically, we will employ nonparametric interaction tree–based approaches integrated with mixed models for repeated measures, together with complementary regression-based methods, to systematically assess treatment–covariate interactions and identify treatment-responsive subpopulations. By uncovering clinically meaningful heterogeneity within trials traditionally viewed as having modest effects, this work aims to repurpose existing AD trial data, advance precision-medicine strategies (Hamburg & Collins, 2010), and inform the design and interpretation of future AD clinical studies.

"
  ["project_specific_aims"]=>
  string(1593) "We propose to apply precision-medicine approaches to identify patient subpopulations that may have benefited from investigational treatments despite negative or inconclusive primary outcomes. Unlike conventional confirmatory subgroup analyses that examine a small number of prespecified subsets, exploratory subgroup identification is conducted post hoc to uncover previously unrecognized heterogeneity in treatment effects. Although many methods have been proposed in the machine learning literature (Lipkovich et al., 2017; Huber et al., 2019), their application in Alzheimer’s disease (AD) trials remains limited, particularly for complex longitudinal outcomes.



Bapineuzumab trials provide a compelling test case. While bapineuzumab failed to demonstrate clinically meaningful efficacy on primary endpoints in phase 3 trials, secondary analyses and biomarker findings revealed substantial heterogeneity in treatment response. Reported differences by APOE ε4 status, disease severity, and amyloid burden, together with minimal decline in many participants, suggest that any clinically meaningful benefit may be concentrated within specific patient subgroups.



Accordingly, our specific aims are to: (1) develop novel tree-based subgroup identification methods capable of detecting treatment-responsive subpopulations when overall treatment effects are modest or null, and (2) apply these methods to bapineuzumab clinical trial data to identify treatment-responsive subgroups and demonstrate the utility of precision-medicine approaches in AD research.

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(8) "meth_res"
    ["label"]=>
    string(23) "Methodological research"
  }
  ["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(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
  }
  ["project_research_methods"]=>
  string(1883) "Explicit Inclusion Criteria (Both Trials)

Patients had to meet all of the following:

1.	Age

o	50–88 years old

2.	Clinical Diagnosis

o	Met criteria for probable Alzheimer’s disease according to the

National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA)

3.	Disease Severity (Cognition)

o	Mini–Mental State Examination (MMSE): 16–26

(Range defines mild-to-moderate Alzheimer’s disease)

4.	Neuroimaging Consistency with AD

o	MRI scan consistent with Alzheimer’s disease

5.	Vascular Burden

o	Hachinski Ischemic Scale ≤ 4

(Low likelihood of vascular dementia)

6.	Medication Stability

o	If taking cognitive enhancers, must be on stable doses of acetylcholinesterase inhibitors or memantine

7.	Genetic Stratification

o	Participants were assigned to one of two trials based on APOE ε4 status:

	Trial 301: APOE ε4 carriers

	Trial 302: APOE ε4 noncarriers

 

Explicit Exclusion Criteria (Both Trials)

Patients were excluded if they had any of the following:

1. Neurologic and Psychiatric Conditions

•	Neurologic disease other than Alzheimer’s disease

•	Major psychiatric disorder

•	History of stroke

•	History of seizures

2. MRI-Based Structural Abnormalities

Screening MRI showing:

•	≥2 microhemorrhages

•	Prior hemorrhage > 1 cm³

•	≥2 lacunar infarcts

•	Prior infarct > 1 cm³

•	Space-occupying lesions

3. Medications

•	Use of cognitive enhancers other than:

o	Acetylcholinesterase inhibitors

o	Memantine

(If used, these had to be at stable doses)



"
  ["project_main_outcome_measure"]=>
  string(2095) "Primary Outcome Measure

Clinical Dementia Rating – Sum of Boxes (CDR-SB)

Domain: Global clinical severity (cognition + function)

Scale: 0–18 (higher scores indicate greater impairment)

Description:

CDR-SB is a composite clinical measure assessing six domains: memory, orientation, judgment/problem solving, community affairs, home/hobbies, and personal care. The sum of domain scores provides a continuous measure of overall disease severity.

Outcome Definition for Analysis:

•	Change in CDR-SB from baseline through Week 78, modeled longitudinally across all scheduled study visits

•	Treatment effect defined as the difference in trajectories (or slope of change) between bapineuzumab and placebo within identified subgroups

Assessment Schedule:

•	Baseline

•	All treatment visits

•	Final visit at Week 78

________________________________________

Secondary Outcome Measures

1. Alzheimer’s Disease Assessment Scale – Cognitive Subscale (ADAS-cog11)

Domain: Cognition

Scale: 0–70 (higher scores indicate worse cognitive impairment)

Description:

A clinician-administered cognitive battery assessing memory, language, attention, and praxis.

Outcome Definition:

•	Change in ADAS-cog11 from baseline to Week 78, analyzed longitudinally across visits

•	Used to validate whether identified subgroups show consistent cognitive treatment effects

________________________________________

2. Disability Assessment for Dementia (DAD)

Domain: Functional ability

Scale: 0–100 (higher scores indicate less functional impairment)

Description:

Assesses basic and instrumental activities of daily living, reflecting patients’ level of independence in everyday functioning.

Outcome Definition:

•	Change in DAD from baseline to Week 78, analyzed longitudinally across visits

•	Used to assess functional relevance of subgroup-specific treatment effects



"
  ["project_main_predictor_indep"]=>
  string(962) "Main Predictor / Independent Variable(s)

1. Treatment Assignment (Primary Independent Variable)

Variable: Bapineuzumab vs. Placebo

Type: Randomized, categorical, time-invariant

Definition

Participants were randomized to receive intravenous bapineuzumab or placebo, administered every 13 weeks for up to 78 weeks. The primary independent variable is the assigned treatment group, representing exposure to the anti–amyloid-β monoclonal antibody.

Coding by Trial

Because the program consisted of two parallel trials stratified by APOE ε4 status, treatment assignment was defined as:

•	APOE ε4 Carrier Trial (Study 301):

o	Bapineuzumab 0.5 mg/kg

o	Placebo

•	APOE ε4 Noncarrier Trial (Study 302):

o	Bapineuzumab 0.5 mg/kg

o	Bapineuzumab 1.0 mg/kg

o	Placebo

(The 2.0 mg/kg arm was discontinued early and excluded from efficacy analyses.)



"
  ["project_other_variables_interest"]=>
  string(1840) "Time / Visit Variables: Scheduled study visit (categorical time) used to model longitudinal trajectories over the 78-week follow-up; treatment × visit interaction to assess differential change over time between bapineuzumab and placebo.



Baseline Disease Severity / Clinical Covariates: Baseline MMSE category (≤21 vs ≥22) for stratification and adjustment; baseline outcome scores including CDR-SB, ADAS-cog11, and DAD to control for initial disease severity.



Genetic Variables: APOE ε4 carrier status (carrier vs noncarrier), which defined the parallel trials; APOE ε4 copy number (1 vs 2 alleles) in the carrier cohort, used as a covariate and effect modifier.



Demographic Variables: Age (continuous), sex (male/female), and race (self-reported; primarily White vs non-White), used for descriptive summaries, covariate adjustment, and subgroup analyses.



Medication Use: Baseline use of cognitive enhancers, including acetylcholinesterase inhibitors and memantine (yes/no), used as stratification factors and covariates.



Biomarker Variables: Global cortical amyloid burden measured by PIB-PET SUVR (with amyloid positivity defined as SUVR ≥1.35 for substudy inclusion); cerebrospinal fluid phospho-tau (p-tau181, pg/mL) as a marker of neurodegeneration; whole-brain volume change (ml/year) measured by structural MRI using the brain boundary-shift integral method.



Additional Clinical Measures: Neuropsychological Test Battery (standardized z-score), MMSE (0–30), and Dependence Scale (0–15) for validation and sensitivity analyses.



Safety Variables: Amyloid-related imaging abnormalities with edema/effusion (ARIA-E), overall and serious adverse events, fatal adverse events, dose level (mg/kg), and MRI safety findings."
  ["project_stat_analysis_plan"]=>
  string(3091) "Below is our planned analytic workflow for the requested AD clinical trial data, moving from descriptive summaries to bivariate and multivariable modeling, and then to advanced subgroup/precision-medicine analyses consistent with our proposal. 

1) Data preparation and cohort definition

•	Harmonize variable definitions across trials (baseline covariates, visit windows, endpoint scales, treatment exposure, follow-up).

•	Define analysis populations: ITT (primary), safety set (as available), and per-protocol sensitivity set.

•	Construct longitudinal outcomes as change from baseline at each visit (primary for MMRM), plus derived summaries (e.g., individual slope) for sensitivity analyses.

•	Missing data assessment (patterns by arm/visit); primary analyses rely on likelihood-based methods under MAR; add sensitivity checks.

2) Descriptive analyses

•	Participant flow: enrollment, randomization, follow-up completion, discontinuation reasons.

•	Baseline characteristics overall and by arm: demographics (age, sex, race/ethnicity), disease severity/stage, baseline cognitive/functional scores (e.g., CDR-SB, ADAS-Cog, ADL), genotype (e.g., APOE4), key biomarkers if available.

•	Outcome summaries by visit and arm: means/SDs (and medians/IQR if skewed), trajectories over time, distribution of change scores, and proportion with minimal/no progression.

3) Bivariate (unadjusted) analyses

•	Baseline balance checks: standardized mean differences; hypothesis tests as secondary.

•	Arm comparisons at key time points: difference in mean change from baseline; effect sizes with CIs.

•	Exploratory associations: baseline covariates vs outcomes (correlations, trend tests, stratified trajectories).

•	Safety/tolerability (if available): AE rates and discontinuation by arm and by baseline strata.

4) Multivariable (adjusted) analyses

•	Primary longitudinal model: Mixed Model for Repeated Measures (MMRM)

o	Outcome: change from baseline across visits.

o	Fixed effects: treatment, visit, treatment×visit; adjust for baseline outcome and key prognostic covariates; unstructured (or appropriate) within-subject covariance.

o	Estimate treatment effects at clinically relevant time points via contrasts; report adjusted means, differences, CIs, and p-values.

5) Planned advanced analyses (precision medicine / subgroup identification)

•	Interaction Tree integrated with MMRM (IT–MMRM):

o	Recursive partitioning driven by treatment×covariate interaction signals to identify subgroups with heterogeneous longitudinal treatment effects (Su et al. 2011).

o	Internal validation via sample-splitting/CV/bootstrapping; control false discoveries through pruning/validation rules.

6) Reporting

•	Pre-specify primary endpoint/time point(s) and key moderators; provide reproducible code, model diagnostics, and sensitivity analyses (missingness, covariance structure, influential sites).



"
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(437) "We plan to complete this study within 12 months. During the first three months following data receipt, we will focus on data familiarization, cleaning, and preliminary descriptive analyses. The proposed methods will then be applied to the data over the subsequent nine months, including model development, validation, and interpretation of results. We anticipate submitting the resulting manuscript within one year of receiving the data."
  ["project_dissemination_plan"]=>
  string(336) "We plan to submit the manuscript to a leading Alzheimer’s disease clinical journal, such as Alzheimer’s & Dementia, Alzheimer’s Research & Therapy, or the Journal of Alzheimer’s Disease. The manuscript is targeted to biostatisticians and clinicians with interests in AD research, clinical trials, and precision medicine."
  ["project_bibliography"]=>
  string(1651) "
Alzheimer’s Association. (2025). Alzheimer’s disease facts and figures. Alzheimer’s & Dementia. DOI: 10.1002/alz.70235
Davidson J, Irizarry M, Bray B, et al. (2010). An exploration of cognitive subgroups in Alzheimer’s disease. Journal of the International Neuropsychological Society, 16(2), 233-243.
Hamburg MA, Collins FS (2010). The path to personalized medicine. New England Journal of Medicine. 363, 301-4.
Huber C, Benda N, Friede T. (2019). A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations. Pharm Stat. 18(5), 600-626.
Lipkovich I, Dmitrienko A, Ralph B. (2017). Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials. Statistics in Medicine. 36: 136–196.
Salloway S, Sperling R, Fox NC, Blennow K, Klunk W, Raskind M, Sabbagh M, Honig LS, Porsteinsson AP, Ferris S, Reichert M, Ketter N, Nejadnik B, Guenzler V, Miloslavsky M, Wang D, Lu Y, Lull J, Tudor IC, Liu E, Grundman M, Yuen E, Black R, Brashear HR; Bapineuzumab 301 and 302 Clinical Trial Investigators. (2014). Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med. 370(4):322-33
Su XG, Meneses K, McNees P, Johnson WO (2011). Interaction trees: exploring the differential effects of an intervention programme for breast cancer survivors. Journal of the Royal Statistical Society: Series C (Applied Statistics). 60, 457-74.
 
 
 
"
  ["project_suppl_material"]=>
  bool(false)
  ["project_coi"]=>
  array(3) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18610)
        ["id"]=>
        int(18610)
        ["title"]=>
        string(9) "COI-1.pdf"
        ["filename"]=>
        string(9) "COI-1.pdf"
        ["filesize"]=>
        int(18452)
        ["url"]=>
        string(58) "https://yoda.yale.edu/wp-content/uploads/2026/01/COI-1.pdf"
        ["link"]=>
        string(57) "https://yoda.yale.edu/data-request/2026-0048/coi-1-pdf-2/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2304"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(11) "coi-1-pdf-2"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18609)
        ["date"]=>
        string(19) "2026-01-19 18:45:21"
        ["modified"]=>
        string(19) "2026-01-19 18:45:22"
        ["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"
      }
    }
    [1]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18648)
        ["id"]=>
        int(18648)
        ["title"]=>
        string(10) "COI_Ke.pdf"
        ["filename"]=>
        string(10) "COI_Ke.pdf"
        ["filesize"]=>
        int(21351)
        ["url"]=>
        string(59) "https://yoda.yale.edu/wp-content/uploads/2026/01/COI_Ke.pdf"
        ["link"]=>
        string(56) "https://yoda.yale.edu/data-request/2026-0048/coi_ke-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2304"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(10) "coi_ke-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18609)
        ["date"]=>
        string(19) "2026-01-22 17:26:38"
        ["modified"]=>
        string(19) "2026-01-22 17:26:41"
        ["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"
      }
    }
    [2]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18649)
        ["id"]=>
        int(18649)
        ["title"]=>
        string(18) "COI-Xu-Zhichen.pdf"
        ["filename"]=>
        string(18) "COI-Xu-Zhichen.pdf"
        ["filesize"]=>
        int(21414)
        ["url"]=>
        string(67) "https://yoda.yale.edu/wp-content/uploads/2026/01/COI-Xu-Zhichen.pdf"
        ["link"]=>
        string(64) "https://yoda.yale.edu/data-request/2026-0048/coi-xu-zhichen-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2304"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(18) "coi-xu-zhichen-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18609)
        ["date"]=>
        string(19) "2026-01-22 17:26:40"
        ["modified"]=>
        string(19) "2026-01-22 17:26:41"
        ["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(4) "2026"
  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  array(21) {
    ["ID"]=>
    int(18831)
    ["id"]=>
    int(18831)
    ["title"]=>
    string(47) "YODA Project Due Diligence Assessment 2026-0048"
    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2026-0048.pdf"
    ["filesize"]=>
    int(126396)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2026/01/YODA-Project-Due-Diligence-Assessment-2026-0048.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2026-0048/yoda-project-due-diligence-assessment-2026-0048/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2026-0048"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18609)
    ["date"]=>
    string(19) "2026-02-26 14:19:32"
    ["modified"]=>
    string(19) "2026-02-26 14:19:32"
    ["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_title_link"]=>
  array(21) {
    ["ID"]=>
    int(18832)
    ["id"]=>
    int(18832)
    ["title"]=>
    string(46) "YODA Project Protocol - 2026-0048 - 2026-01-22"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2026-0048-2026-01-22.pdf"
    ["filesize"]=>
    int(184562)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2026/01/YODA-Project-Protocol-2026-0048-2026-01-22.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2026-0048/yoda-project-protocol-2026-0048-2026-01-22/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2026-0048-2026-01-22"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18609)
    ["date"]=>
    string(19) "2026-02-26 14:19:53"
    ["modified"]=>
    string(19) "2026-02-26 14:19:53"
    ["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"]=>
  array(21) {
    ["ID"]=>
    int(18833)
    ["id"]=>
    int(18833)
    ["title"]=>
    string(36) "YODA Project Review - 2026-0048_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2026-0048_site.pdf"
    ["filesize"]=>
    int(1331975)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2026/01/YODA-Project-Review-2026-0048_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2026-0048/yoda-project-review-2026-0048_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2026-0048_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18609)
    ["date"]=>
    string(19) "2026-02-26 14:20:12"
    ["modified"]=>
    string(19) "2026-02-26 14:20:12"
    ["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(0) ""
  ["project_funding_source"]=>
  string(17) "NIH R21 AG084054 "
}
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"
}