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  ["project_title"]=>
  string(148) "Calibrating and Validating Individualized Counterfactual PANSS Symptom-Trajectory Forecasts in Schizophrenia Using Randomized and Real-World Cohorts"
  ["project_narrative_summary"]=>
  string(835) "Schizophrenia is usually treated by trial-and-error, and clinicians have few tools to predict how a patient's symptoms will change under one treatment versus another. This project tests a way to make that prediction. Using de-identified data from completed schizophrenia trials shared through the YODA Project, we first measure how treatment changes symptoms on the widely used PANSS rating scale, then use those measurements to forecast, for an individual patient, how their symptoms would likely unfold under a chosen treatment. We check these forecasts against what actually happened in real-world patients - including a pragmatic cohort and untreated comparison groups - and in a separate independent cohort. The goal is a validated, practical method for forecasting individual treatment response to support more personalized care."
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  array(7) {
    ["first_name"]=>
    string(7) "Jinyuan"
    ["last_name"]=>
    string(3) "Liu"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(21) "Vanderbilt University"
    ["email"]=>
    string(20) "jinyuan.liu@vumc.org"
    ["state_or_province"]=>
    string(2) "TN"
    ["country"]=>
    string(13) "United States"
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      ["p_pers_f_name"]=>
      string(6) "Haoyue"
      ["p_pers_l_name"]=>
      string(2) "Li"
      ["p_pers_degree"]=>
      string(2) "MS"
      ["p_pers_pr_affil"]=>
      string(21) "Vanderbilt University"
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      ["requires_data_access"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
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  ["property_scientific_abstract"]=>
  string(1475) "Background: Randomized trials establish average treatment effects in schizophrenia but do not tell a clinician how an individual patient's symptoms will evolve under a chosen treatment, or when to intervene. Objective: To calibrate trial-derived treatment effects and validate individualized counterfactual forecasts of PANSS symptom trajectories in randomized and real-world cohorts. Study Design: Methodological research using participant-level data from Johnson & Johnson-sponsored schizophrenia trials (4 double-blind RCTs and 1 pragmatic trial; 12-27 month follow-up) accessed via YODA, with external validation. Participants: Adults with DSM-IV/5 schizophrenia, schizoaffective, or schizophreniform disorder; double-blind treatment arms (~1,600) for calibration, and a pragmatic cohort (~600) and pooled placebo arms (~600) for validation. Primary and Secondary Outcome Measures: Primary, individualized PANSS symptom trajectories at item and five-factor domain levels (forecast vs. observed); secondary, therapeutic-window timing at which distress-targeted intervention is predicted to yield clinically meaningful improvement. Statistical Analysis: Bayesian multivariate mixed-effects models estimate and calibrate treatment effects, then transport them via transfer learning to generate individualized counterfactual forecasts with quantified uncertainty, benchmarked by concordance correlation, calibration slope, RMSE, and time-dependent AUC with bootstrap CIs."
  ["project_brief_bg"]=>
  string(2280) "Schizophrenia affects 3.7 million U.S. adults, shortens life expectancy by nearly 30 years [1,2], and carries a large annual economic burden [3]. Even with effective pharmacologic treatments, care remains largely trial-and-error: up to one-third of patients fail to respond to first-line therapy [4,5], and clinicians lack tools to anticipate how a given patient will respond before treatment begins. Randomized controlled trials (RCTs) rigorously estimate average treatment effects, but average effects do not tell a clinician how an individual patient's symptoms will unfold under a chosen treatment, or when intervention is likely to help most. The 30-item Positive and Negative Syndrome Scale (PANSS) [6] is collected in nearly every trial and clinic, yet is used to quantify severity rather than to forecast individual response.

This proposal focuses on individualized counterfactual forecasting of PANSS symptom trajectories. We treat a patient as a "digital twin" whose baseline symptom state is propagated forward under alternative treatment strategies [7,8], calibrating treatment effects from RCTs and transporting them to real-world populations, where RCT-derived effects are otherwise difficult to generalize [24]. We focus on distress symptoms (anxiety, guilt, tension, depression) as a clinically urgent, readily modifiable, and psychotherapy-prioritized target; an RCT from our group validated distress as a high-impact, modifiable target using worry-focused psychotherapy [9,10].

Preliminary evidence supports feasibility: prototype digital twins reproduced and transported RCT treatment effects (concordance correlation coefficient [CCC]=0.89), forecast symptom trajectories beyond trial follow-up, and identified therapeutic windows for intervention. Building on this, the present work will calibrate treatment effects across five trials and validate individualized trajectory forecasts in a pragmatic cohort and pooled placebo arms, with external validation in an independent cohort. Establishing that individualized forecasts reproduce observed trajectories and remain calibrated across randomized and real-world settings is a necessary step toward using routinely collected PANSS data to support personalized treatment planning in schizophrenia."
  ["project_specific_aims"]=>
  string(1154) "Specific Aim. To determine when intervention is expected to be most effective, we will estimate and calibrate treatment effects from five randomized trials and apply them counterfactually to a pragmatic cohort (N~600; concurrent medications) and a pooled placebo cohort (N~600; no medication) to generate individualized PANSS symptom-trajectory forecasts with quantified uncertainty, evaluating external generalizability in an independent cohort (N~450).

Approach: (1) calibrate trial-derived treatment effects at the PANSS item and domain levels; (2) transport calibrated effects to the real-world cohorts via transfer learning; (3) simulate alternative treatment timing and duration to identify therapeutic windows; (4) forecast longer-term benefit across follow-up; and (5) validate forecasts in independent cohorts.

H2a: Individualized forecasts will reproduce observed symptom trajectories while remaining well calibrated across trials and external cohorts. H2b: Forecasts will identify therapeutic windows during which distress-targeted intervention is predicted to produce clinically meaningful improvement beyond usual care.

"
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    ["label"]=>
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  ["project_purposes"]=>
  array(6) {
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      ["label"]=>
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    [1]=>
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      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [2]=>
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      ["value"]=>
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      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
    [3]=>
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      ["label"]=>
      string(37) "Develop or refine statistical methods"
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      ["label"]=>
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    }
  }
  ["project_research_methods"]=>
  string(1248) "Data will be accessed through the YODA Project [16], comprising Johnson & Johnson-sponsored individual participant data from five schizophrenia trials [11-15]: four double-blind RCTs (NCT01529515, NCT01193153, NCT00111189, NCT00086320) and one pragmatic trial (NCT00216476); 12-27 month follow-up. For this project, the double-blind treatment arms (~1,600) are used to calibrate treatment effects; the pragmatic cohort (~600) and pooled placebo arms (~600) are used to validate individualized trajectory forecasts.

Inclusion criteria: confirmed DSM-IV/5 diagnosis of schizophrenia, schizoaffective, or schizophreniform disorder.

Exclusion criteria: major medical/neurologic illness; IQ <75; substance use disorder within the previous 3 months.

External validation cohort (NOT obtained through YODA): an independent cohort (N~450) obtained from [SOURCE/OWNER - to confirm]. All YODA participant-level analyses will be conducted within the YODA secure platform; no participant-level YODA data will be exported. The external cohort will be pooled with YODA IPD only if it can be uploaded into the secure platform under appropriate rights/license; otherwise it will be analyzed separately and combined at the summary level. "
  ["project_main_outcome_measure"]=>
  string(872) "Primary outcome: individualized PANSS symptom trajectories, evaluated at the item and five-factor domain levels as forecast versus observed change over follow-up (12-27 months). Forecast accuracy and calibration are assessed by concordance correlation, calibration slope, RMSE, and time-dependent AUC, with bootstrap confidence intervals.

Secondary outcome: therapeutic-window timing - the treatment initiation window and duration during which distress-targeted intervention is predicted to produce clinically meaningful improvement beyond usual care, summarized as item- and domain-level trajectory divergence with uncertainty intervals.

Outcomes are defined consistently across the randomized, pragmatic, and external cohorts to the extent the source data allow; any cohort-specific differences in PANSS administration or timing will be documented.

"
  ["project_main_predictor_indep"]=>
  string(726) "Main independent variable: treatment assignment - active pharmacologic treatment (double-blind treatment arm) versus placebo (double-blind placebo arm); in the pragmatic cohort, treatment received under concomitant medications. Treatment/arm is coded as a binary indicator.

In the counterfactual forecasts, the manipulated inputs are: (i) the symptom-target perturbation - modulation of distress symptoms (anxiety, guilt, tension, depression) applied to baseline PANSS states, defined as specified changes to targeted PANSS item scores; and (ii) treatment timing (initiation window, e.g., 0-3 vs. 6-8 months after psychosis onset) and duration (8 vs. 25 weeks), which are varied to identify therapeutic windows.

"
  ["project_other_variables_interest"]=>
  string(603) "Covariates used to characterize the sample and to calibrate and adjust treatment-effect models include: age, sex, race/ethnicity and other available demographics; diagnostic subtype (schizophrenia, schizoaffective, schizophreniform); concomitant medications (a key covariate distinguishing the pragmatic cohort from the pooled placebo arms); baseline PANSS item and five-factor domain scores; illness duration/chronicity where available; and time since psychosis onset (used in timing analyses). Continuous covariates retain their measured scale; categorical covariates use the categories listed above. "
  ["project_stat_analysis_plan"]=>
  string(2637) "Overview. Treatment effects are calibrated from randomized arms and transported to real-world cohorts to generate individualized counterfactual PANSS symptom-trajectory forecasts, benchmarked throughout by concordance correlation (CCC), calibration slope, RMSE, and time-dependent AUC with bootstrap confidence intervals [26,27].

Step 1 - Calibrate trial-derived treatment effects. Building on preliminary Bayesian multivariate mixed-effects (B-mix) models fit to a worry-focused CBTp RCT [17,18,19], we develop a grand model pooling all five RCT treatment arms (N~1,600) [20], incorporating patient-level covariates (e.g., concomitant medications) and arm-specific random slopes [21,22]. Posterior distributions yield individualized treatment-effect estimates with uncertainty intervals. Models are fit at the PANSS item and domain levels to preserve inter-item dependence rather than relying on composite scores. Performance is benchmarked against all five RCTs using concordance correlation, calibration slope, and RMSE, with uncertainty from 5,000 bootstrap resamples.

Step 2 - Transport treatment effects to real-world cohorts. Baseline PANSS profiles and clinical covariates from the pragmatic cohort (N~600) and the pooled placebo cohort (N~600) are entered separately into the RCT-calibrated B-mix models. Transfer learning transports calibrated effects into these naturalistic populations [23,24]; counterfactual predictions estimate outcomes as if patients had received the intervention, summarized as percentage change at item and domain levels.

Step 3 - Optimize treatment timing and duration. Using the individualized counterfactual trajectories from Step 2, we simulate alternative schedules by varying initiation (0-3 vs. 6-8 months after psychosis onset) and duration (8 vs. 25 weeks) [25]. Outcomes include patient-specific PANSS trajectories at item and domain levels, AUC summaries, and uncertainty intervals, identifying individualized therapeutic windows.

Step 4 - Forecast long-term treatment benefit. Factual versus counterfactual PANSS trajectories are compared over 12-27 month follow-up; long-term benefit is quantified by trajectory divergence using time-averaged AUC, stratified by domain.

Step 5 - Validate trajectory forecasts. Forecasts of trajectories and therapeutic windows are replicated in an independent cohort (N~450) that resembles the development pragmatic cohort. Agreement with the development cohort is assessed by concordance correlation, calibration slope, and time-dependent AUC with bootstrap CIs, establishing external reproducibility [26,27,28].

"
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      ["value"]=>
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      ["label"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(1207) "The Data Use Agreement provides 12 months of platform access, with extension possible. Anticipated milestones (relative to data access = Month 0):

Months 0-1: Environment setup, data familiarization, cohort construction, quality checks.

Months 1-5 (Step 1): Fit and calibrate Bayesian multivariate mixed-effects treatment-effect models across the five RCT treatment arms; benchmark reproduction of observed treatment-induced change.

Months 4-8 (Steps 2-3): Transport calibrated effects to the pragmatic and pooled placebo cohorts; generate individualized counterfactual trajectory forecasts; simulate timing/duration to identify therapeutic windows.

Months 7-10 (Step 4): Forecast long-term benefit across follow-up; sensitivity analyses.

Months 9-11 (Step 5): External validation in the independent cohort; calibration and reproducibility assessment.

Months 10-12: Manuscript drafting and R-package preparation; first submission for publication anticipated by Month 12. Results reported back to the YODA Project within 30 days of submission and upon publication. An extension will be requested if external validation or revisions require additional access.

"
  ["project_dissemination_plan"]=>
  string(1240) "Anticipated products include one to two peer-reviewed manuscripts presenting the calibration-and-transport framework and the validation of individualized counterfactual PANSS symptom-trajectory forecasts (including therapeutic-window analyses) across randomized and real-world cohorts. A key deliverable is an open-source R package implementing the calibrated Bayesian treatment-effect models and the counterfactual forecasting workflow, with documentation and reproducible examples so that other investigators can apply the method to their own PANSS data. Target audiences are psychiatric researchers, biostatisticians/methodologists, and clinicians in precision psychiatry. Suitable journals include JAMA Psychiatry, Lancet Psychiatry, Molecular Psychiatry, Schizophrenia Bulletin, npj Digital Medicine, and Schizophrenia Research. Findings will also be presented at scientific meetings (e.g., SIRS, ACNP) and disseminated through preprints and openly documented analysis code. Results, including a summary of findings, will be reported back to the YODA Project as required; no participant-level data will leave the secure platform. Materials will acknowledge the YODA Project and the trial sponsor consistent with the Data Use Agreement."
  ["project_bibliography"]=>
  string(5005) "

Correll, C. U. et al. Mortality in people with schizophrenia: a systematic review and meta-analysis of relative risk and aggravating or attenuating factors. World Psychiatry 21, 248-271 (2022).
Olfson, M., Gerhard, T., Huang, C., Crystal, S. & Stroup, T. S. Premature Mortality Among Adults With Schizophrenia in the United States. JAMA Psychiatry 72, 1172 (2015).
Kadakia, A. et al. The Economic Burden of Schizophrenia in the United States. J. Clin. Psychiatry 83, (2022).
Potkin, S. G. et al. The neurobiology of treatment-resistant schizophrenia: paths to antipsychotic resistance and a roadmap for future research. Npj Schizophr. 6, 1 (2020).
Howes, O. D. et al. Treatment-Resistant Schizophrenia: TRRIP Working Group Consensus Guidelines on Diagnosis and Terminology. Am. J. Psychiatry 174, 216-229 (2017).
Kay, S. R., Fiszbein, A. & Opler, L. A. The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophr. Bull. 13, 261-276 (1987).
Laubenbacher, R., Mehrad, B., Shmulevich, I. & Trayanova, N. Digital twins in medicine. Nat. Comput. Sci. 4, 184-191 (2024).
Spitzer, M., Dattner, I. & Zilcha-Mano, S. Digital twins and the future of precision mental health. Front. Psychiatry 14, 1082598 (2023).
Freeman, D. et al. Effects of cognitive behaviour therapy for worry on persecutory delusions in patients with psychosis (WIT): a parallel, single-blind, randomised controlled trial with a mediation analysis. Lancet Psychiatry 2, 305-313 (2015).
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