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  ["project_title"]=>
  string(99) "Integrative Analysis of Treatment Effect of Esketamine By Incorporating AI-Generated Synthetic Data"
  ["project_narrative_summary"]=>
  string(848) "Esketamine has been studied in multiple randomized clinical trials for depression, yet treatment response varies substantially across patients and trial settings. This project will conduct an integrative analysis of 15 esketamine trials available through the YODA Project using individual participant level data. We will harmonize endpoints and covariates across trials and estimate both overall and subgroup treatment effects. A distinctive component is the use of AI generated synthetic data as a principled augmentation and validation tool. We will train modern generative models within the secure analysis environment to produce synthetic records without exposing participant identities. The final deliverables will include estimates of treatment effect, and a transparent workflow for responsible synthetic data assisted integrative analysis. "
  ["project_learn_source"]=>
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  array(7) {
    ["first_name"]=>
    string(5) "Jiwei"
    ["last_name"]=>
    string(4) "Zhao"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(31) "University of Wisconsin-Madison"
    ["email"]=>
    string(19) "jiwei.zhao@wisc.edu"
    ["state_or_province"]=>
    string(2) "WI"
    ["country"]=>
    string(13) "United States"
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      ["p_pers_f_name"]=>
      string(6) "Jiawei"
      ["p_pers_l_name"]=>
      string(4) "Shan"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(31) "University of Wisconsin-Madison"
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      ["requires_data_access"]=>
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  ["project_ext_grants"]=>
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    ["label"]=>
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  ["property_scientific_abstract"]=>
  string(1651) "Background: Esketamine trials span diverse populations and study designs. This heterogeneity complicates synthesis of treatment effects and identification of patients most likely to benefit. 



Objective: This project is to estimate overall and subgroup treatment effects of esketamine across 15 YODA trials by integrating participant level data and incorporating AI generated synthetic data.



Study Design: Retrospective integrative analysis of participant level data. We will harmonize covariates and outcomes across trials and evaluate rapid and short term response patterns as well as longer term endpoints when available (e.g., relapse prevention, long term follow up). 



Participants: All randomized participants in the requested YODA esketamine trials meeting analysis ready criteria (baseline assessment and ≥1 post baseline outcome). 



Outcomes: Primary outcome will be change in depression severity measured by standard trial instruments frequently used in these studies (e.g., MADRS total). Secondary outcomes include response/remission, functional impairment measures (e.g., SDS), cognition (e.g., HVLT / cognitive batteries where available), and suicidality outcomes in suicidality focused studies. 



Statistical Analysis: We will estimate average treatment effects (ATE) and conditional treatment effects (CATE) using doubly robust causal learners (e.g., DR Learner/AIPW), causal forests, and deep learning models. Synthetic data generated by deep generative models will support model development, robustness checks, and small subgroup sensitivity analyses. 

"
  ["project_brief_bg"]=>
  string(1862) "Brief Project Background



Esketamine is an NMDA receptor antagonist studied across multiple randomized clinical trials in TRD and related depressive presentations, including studies focusing on rapid reduction of depressive symptoms in the context of acute suicidal ideation/behavior [2,3]. The evidence base includes short term efficacy studies, relapse prevention trials, long term safety follow up, and subgroup cohorts such as elderly participants. Prior YODA applications illustrate the feasibility of harmonizing trial level endpoints (e.g., MADRS) and conducting integrative analyses or ML based prediction in depression datasets. 



Statement of Project Significance



Despite this extensive trial portfolio, clinically meaningful heterogeneity remains: some patients respond rapidly and robustly, while others show minimal improvement. Integrative participant level analyses can improve precision of treatment effect estimates and help identify which baseline clinical features predict benefit. However, subgroup analyses may be underpowered when strata are small or when missingness is differential across studies. Synthetic data—generated within a controlled, privacy preserving framework—can be used to stress test robustness, support model selection, and explore stability of subgroup findings without exposing individual identities. 



This project is significant because it will (1) deliver harmonized, cross trial estimates of esketamine’s effects over clinically relevant time horizons, (2) identify interpretable predictors of differential response to inform precision psychiatry, and (3) provide a generalizable methodological blueprint for responsible synthetic data assisted integrative analyses aligned with emerging AI/ML approaches demonstrated in prior YODA-style work. 

"
  ["project_specific_aims"]=>
  string(1646) "Aim 1: Harmonize and integrate participant level data across 15 esketamine trials.

Rationale: Trials vary by design, population, endpoints, and visit schedules; harmonization is required for valid synthesis. 

Approach: Create a common data model (CDM) for baseline covariates, dosing/arm definitions, and outcome time windows; document all mappings and derivations for reproducibility. 



Aim 2: Estimate overall treatment effects (ATE) and heterogeneity of treatment effects (CATE) for esketamine versus control.

Rationale: Average effects may obscure clinically important heterogeneity. 

Hypothesis: Treatment effects vary by baseline severity, prior treatment history/TRD status, age (including elderly cohorts), and suicidality related baseline measures (where applicable). 

Approach: Use causal ML methods with cross trial hierarchical pooling to estimate ATE/CATE and quantify uncertainty. 



Aim 3: Develop and evaluate AI generated synthetic data to augment robustness and sensitivity analyses [1,4].

Rationale: Synthetic data can support model development, robustness checks, and small subgroup sensitivity while reducing privacy risks. 

Hypothesis: Synthetic assisted model development improves stability of prediction/heterogeneity estimates and reduces variance of subgroup contrasts without changing primary conclusions derived from real data. 

Approach: Train deep generative models on harmonized trial data, evaluate fidelity and privacy risk, and use synthetic data only for prespecified auxiliary analyses (not primary effect estimation). 

"
  ["project_study_design"]=>
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      ["label"]=>
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    [1]=>
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      string(59) "preliminary_research_to_be_used_as_part_of_a_grant_proposal"
      ["label"]=>
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    [2]=>
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      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
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      ["label"]=>
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  string(340) "Data Source: Individual participant level data (IPD) from 15 YODA esketamine trials, including full CSR and supporting documentation as available. 



Inclusion criteria: We will include all participants from the requested YODA trials. We will not include any additional patients.



Exclusion criteria: None

"
  ["project_main_outcome_measure"]=>
  string(315) "Primary: Change in depression severity (e.g., MADRS total score; item level analyses when feasible across trials). 



Secondary: Response/remission, functional impairment (e.g., SDS), cognition (e.g., HVLT/cognitive batteries where present), suicidality metrics in suicidality-focused studies. 

"
  ["project_main_predictor_indep"]=>
  string(186) "Treatment assignment (esketamine regimen + background therapy vs placebo/active comparator + background therapy, as defined per protocol), harmonized across trials into comparable arms. "
  ["project_other_variables_interest"]=>
  string(227) "Demographics (age, sex), baseline severity, prior treatment history/TRD indicators, baseline suicidality (when available), concomitant oral antidepressant class, site/region, dosing/frequency, and relevant protocol covariates. "
  ["project_stat_analysis_plan"]=>
  string(1960) "Data Harmonization Plan

Create a crosswalk for variable names/units/scales and visit timing across trials (baseline definition, post baseline windows). 

Define common time points (e.g., rapid endpoint windows such as ~24h when available; short term endpoints such as ~4 weeks) and trial specific mapping rules. 

Quality control: range checks, consistency checks against CSR summaries, and reproducible audit logs. 



Statistical Analysis Plan

Descriptive analyses: baseline comparability by arm and trial; outcome distributions and missingness patterns. 

ATE estimation: mixed effects models / hierarchical Bayesian IPD meta analysis and doubly robust estimators with trial indicators. 

CATE estimation: causal forests, DR Learner/T Learner, BART, and interpretable ML with cross fitting to estimate heterogeneity and prevent overfitting. 

Multiple comparisons: control FWER/FDR for prespecified subgroup contrasts (e.g., age strata, baseline severity strata). 

Validation: leave one trial out validation to assess generalizability across studies. 



Synthetic Data Generation and Use

Generation models: privacy aware deep generative models for tabular/longitudinal data (e.g., DP VAE, tabular diffusion; optionally GAN variants with privacy audits). 

Evaluation: compare marginal distributions, correlations, and predictive utility; run privacy risk assessments (e.g., membership inference tests) within the secure platform. 

How synthetic data will be used: robustness checks for missingness, distribution shift, and small subgroup stability; simulation-based sensitivity analyses (e.g., under represented elderly cohorts). 

How synthetic data will NOT be used: It will not replace real participant outcomes for primary ATE/CATE reporting; primary estimates will come from real IPD, with synthetic analyses labeled as sensitivity/auxiliary.

"
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      ["label"]=>
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  ["project_timeline"]=>
  string(244) "Start date: February 2026 or as soon as possible

Analysis completion date: August 2026

Date manuscript drafted: December 2026

First submitted for publication: January 2027

Results reported back: February 2027

"
  ["project_dissemination_plan"]=>
  string(497) "Anticipated products: all R/Python code necessary to replicate the numerical results will be provided in a publicly accessible GitHub repository.



Target audience: Researchers in statistics, biostatistics, machine learning, biomedical data science and mental health



Expectation for study manuscript and journals: Biostatistics, Biometrics, Journal of the American Statistical Association, Annals of Applied Statistics, ML conferences such as NeurIPS/ICML/ICLR.

"
  ["project_bibliography"]=>
  string(957) "
[1] Azizi, Z., Zheng, C., Mosquera, L., et al. (2021) Can synthetic data be a proxy for real clinical trial data? A validation study. BMJ Open, 11, e043497.
[2] Canuso CM, Ionescu DF, Li X, Qiu X, Lane R, Turkoz I, Nash AI, Lopena TJ, Fu DJ. Esketamine nasal spray for the rapid reduction of depressive symptoms in major depressive disorder with acute suicidal ideation or behavior. Journal of clinical psychopharmacology. 2021 Sep 1;41(5):516-24.
[3] Horowitz MA, Moncrieff J. Are we repeating mistakes of the past? A review of the evidence for esketamine. The British Journal of Psychiatry. 2021 Nov;219(5):614-7.
[4] Koul, A., Duran, D. and Hernandez-Boussard, T. (2025) Synthetic Data, Synthetic Trust: Navigating Data Challenges in the Digital Revolution. The Lancet Digital Health, 7.
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    string(19) "2026-03-26 13:50:39"
    ["modified"]=>
    string(19) "2026-03-26 13:50:39"
    ["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(18991)
    ["id"]=>
    int(18991)
    ["title"]=>
    string(36) "YODA Project Review - 2026-0088_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2026-0088_site.pdf"
    ["filesize"]=>
    int(1331912)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2026/02/YODA-Project-Review-2026-0088_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2026-0088/yoda-project-review-2026-0088_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2026-0088_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18715)
    ["date"]=>
    string(19) "2026-03-26 13:51:00"
    ["modified"]=>
    string(19) "2026-03-26 13:51:00"
    ["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) ""
}
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"
}