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  ["project_title"]=>
  string(86) "Trajectories of Relapse and Predictors of Functioning in Antipsychotic Clinical Trials"
  ["project_narrative_summary"]=>
  string(628) "This project will analyze Janssen antipsychotic clinical trial data to examine trajectories of relapse following treatment discontinuation and to identify predictors of functioning. Using individual participant data, we will model heterogeneity in relapse timing  after withdrawal. We will also examine baseline demographic and clinical features  as predictors of functioning and relapse risk. The findings aim to clarify who benefits most from continued antipsychotic treatment and who may maintain stability after discontinuation, supporting more personalized maintenance and discontinuation strategies in psychotic disorders."
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
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    ["first_name"]=>
    string(6) "Robert"
    ["last_name"]=>
    string(10) "McCutcheon"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(17) "Oxford University"
    ["email"]=>
    string(32) "robert.mccutcheon@psych.ox.ac.uk"
    ["state_or_province"]=>
    string(6) "Oxford"
    ["country"]=>
    string(14) "United Kingdom"
  }
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    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Krisya"
      ["p_pers_l_name"]=>
      string(5) "Louie"
      ["p_pers_degree"]=>
      string(3) "MSc"
      ["p_pers_pr_affil"]=>
      string(31) "Chinese University of Hong Kong"
      ["p_pers_scop_id"]=>
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      ["requires_data_access"]=>
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      ["p_pers_f_name"]=>
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      ["p_pers_l_name"]=>
      string(7) "Mercado"
      ["p_pers_degree"]=>
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      ["p_pers_pr_affil"]=>
      string(17) "Oxford University"
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      ["requires_data_access"]=>
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  ["project_ext_grants"]=>
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    ["value"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1660) "Background:

Relapse following antipsychotic discontinuation remains a major challenge in the treatment of schizophrenia and related psychotic disorders. While maintenance therapy reduces relapse risk, prolonged exposure may be associated with functional and metabolic burden. There is limited understanding of individual differences in relapse trajectories and predictors of functioning across treatment and withdrawal phases.



Objective:

To identify trajectories of relapse following antipsychotic discontinuation and to determine baseline and dynamic predictors of functional outcomes during acute treatment and after treatment withdrawal.



Study Design:

Secondary analysis of individual participant-level data from Janssen-sponsored randomized controlled trials of antipsychotic medications, including acute and maintenance phases with discontinuation or withdrawal arms.



Participants:

Adults with schizophrenia or related psychotic disorders enrolled in relevant clinical trials, with available data on treatment exposure, discontinuation, and functional and symptom outcomes.



Primary and Secondary Outcome Measure(s):

Primary: Time to relapse and latent trajectory patterns of relapse post-discontinuation.

Secondary: Functioning scores (e.g., PSP, SOFAS, GAF) at baseline, end of acute treatment, and post-withdrawal; clinical and demographic predictors of functional change.



Statistical Analysis:

Latent class modeling will identify relapse trajectories. Mixed-effects and regression models will examine predictors of functioning. "
  ["project_brief_bg"]=>
  string(3024) "Relapse following antipsychotic discontinuation is one of the most pressing challenges in the long-term management of schizophrenia and related psychotic disorders. Although continuous maintenance therapy reduces relapse risk, indefinite antipsychotic exposure can lead to cumulative adverse effects, including metabolic and neurological burdens. Determining who benefits from ongoing treatment versus who can safely discontinue remains an unresolved and clinically critical question.



Existing evidence suggests that relapse after discontinuation is not uniform some patients relapse rapidly, others after a delay, and some maintain stability for extended periods.  It is not clear if some relapses after antipsychotic discontinuation reflect a withdrawal type reaction  as opposed to an illness relapse.  To determine this we need to analyse withdrawal trajectories using using large, participant-level datasets. Similarly, functional recovery—an outcome distinct from symptomatic improvement—remains underexplored, particularly regarding predictors of functional change during both acute treatment and the discontinuation phase. Understanding the interplay between symptom remission, functioning, and relapse could inform individualized approaches to maintenance and withdrawal strategies.



Antipsychotic clinical trial data, offer a unique opportunity to address these questions using rigorously collected, high-quality participant-level data across multiple compounds, phases, and study designs. By leveraging this resource, the proposed project will:



Characterize relapse trajectories following antipsychotic discontinuation vs continuation  using data-driven approaches (e.g., latent class modeling).



Identify predictors of functioning both at baseline and longitudinally during acute treatment and after discontinuation, considering clinical, demographic, and treatment-related factors.





The results of this research will materially enhance generalizable scientific and clinical understanding by defining patterns of relapse and recovery, informing predictive models for clinical decision-making, and guiding the design of future trials focused on treatment duration and discontinuation strategies. Insights gained will support precision psychiatry initiatives aimed at reducing unnecessary exposure to antipsychotics while maintaining stability and promoting functional recovery.



1.	Wunderink, L., Nieboer, R. M., Wiersma, D., Sytema, S. & Nienhuis, F. J. Recovery in Remitted First-Episode Psychosis at 7 Years of Follow-up of an Early Dose Reduction/Discontinuation or Maintenance Treatment Strategy: Long-term Follow-up of a 2-Year Randomized Clinical Trial. JAMA Psychiatry 1–8 (2013).

2.	McCutcheon, R. A. et al. Does Slow and Steady Win the Race? Rates of Antipsychotic Discontinuation, Antipsychotic Dose, and Risk of Psychotic Relapse. Schizophr. Bull. (2023) doi:10.1093/schbul/sbad139.

"
  ["project_specific_aims"]=>
  string(1436) "Relapse after antipsychotic discontinuation remains a major challenge in the management of schizophrenia and related psychotic disorders, yet the contribution of treatment withdrawal itself versus underlying illness vulnerability remains unclear. Functional recovery is equally important but less well understood, particularly regarding how clinical and demographic factors predict functioning both during treatment and after withdrawal.



Aim 1: To characterize and compare relapse trajectories between antipsychotic continuation and discontinuation.

Using individual participant data randomized controlled trials, we will model relapse trajectories across treatment arms employing latent class modeling. We aim to determine whether relapse timing, trajectory shape, and subgroup membership differ between continuation and discontinuation groups, thereby quantifying the extent to which withdrawal itself contributes to relapse risk beyond illness progression.



Aim 2: To identify predictors of functioning during acute treatment and following discontinuation.

We will examine baseline (e.g., demographics, symptom severity, cognitive status) and dynamic (e.g., symptom change, adherence, treatment duration) predictors of functioning as measured by PSP, SOFAS, or GAF scores. Mixed-effects and regression models will assess predictors of both baseline functioning and change over time.

"
  ["project_study_design"]=>
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    ["label"]=>
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  ["project_purposes"]=>
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      ["label"]=>
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    [1]=>
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      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
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  ["project_research_methods"]=>
  string(426) "Data sources will be randomised controlled trials of antipsychotics in the treatment of schizophrenia



Patients will have:



Diagnosis of schizophrenia, psychosis, or schizoaffective disorder

Age ≥18

Availability of longitudinal clinical and functional measures



Exclusion criteria: Age <18, diagnoses other than schizophrenia, psychosis, or schizoaffective disorder"
  ["project_main_outcome_measure"]=>
  string(240) "Primary outcome measure: Relapse

Positive and Negative Syndrome Scale: Total score, subscales, and individual items

Functional measures: Personal and Social Performance Scale, Global Assessment of Functioning, 



"
  ["project_main_predictor_indep"]=>
  string(465) "Positive and Negative Syndrome Scale: Total score and subscales (positive, negative, general)

Barnes Akathisia Rating Scale

Brief Assessment of Cognition in Schizophrenia

Abnormal Involuntary Movement Scale

Simpson Angus Scale

Schizophrenia Quality of Life Scale

Clinical Global Impression

Personal and Social Performance Scale

Laboratory markers 

Age

Gender

Race/Ethinicity

Weight"
  ["project_other_variables_interest"]=>
  string(40) "The above covers  all relevant variables"
  ["project_stat_analysis_plan"]=>
  string(1045) "Descriptive analyses will summarize baseline and clinical characteristics by treatment arm (continuation vs. discontinuation). Bivariate tests (t-tests, χ², correlations) will examine relationships among predictors, relapse, and functioning.

For the primary analysis, relapse trajectories following discontinuation will be identified using latent class modeling, allowing detection of distinct relapse patterns over time. We will compare the proportion of latent classes between continuation and discontinuation groups to assess how much relapse trajectories rather than overall risk of relapse are shaped by withdrawal. Symptom profiles (e.g., PANSS, CGI) will be compared both between arms and across latent classes to clarify clinical differences among trajectory groups.

Secondary analyses will examine predictors of functioning (e.g., PSP, SOFAS, GAF) using linear mixed-effects and multivariable regression models, incorporating baseline and time-varying covariates such as symptom severity and treatment duration. 

"
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      ["label"]=>
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  ["project_timeline"]=>
  string(95) "3 months - data cleaning

2 months -data analysis

4 months- manuscript preparation"
  ["project_dissemination_plan"]=>
  string(203) "Findings will be disseminated via publication in clinical journals (e.g. JAMA Psychiatry, Lancet Psychiatry) and presentation at scientific conferences (e.g. Schizophrenia International Reserach Society)"
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    ["filesize"]=>
    int(129552)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Due-Diligence-Assessment-2025-0740.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2025-0740/yoda-project-due-diligence-assessment-2025-0740/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2025-0740"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18141)
    ["date"]=>
    string(19) "2026-01-15 15:23:50"
    ["modified"]=>
    string(19) "2026-01-15 15:23:50"
    ["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(18628)
    ["id"]=>
    int(18628)
    ["title"]=>
    string(46) "YODA Project Protocol - 2025-0740 - 2026-01-21"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0740-2026-01-21.pdf"
    ["filesize"]=>
    int(178880)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Protocol-2025-0740-2026-01-21.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0740/yoda-project-protocol-2025-0740-2026-01-21/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0740-2026-01-21"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18141)
    ["date"]=>
    string(19) "2026-01-21 16:00:15"
    ["modified"]=>
    string(19) "2026-01-21 16:00:15"
    ["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(18603)
    ["id"]=>
    int(18603)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0740_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0740_site.pdf"
    ["filesize"]=>
    int(1331870)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Review-2025-0740_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0740/yoda-project-review-2025-0740_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0740_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18141)
    ["date"]=>
    string(19) "2026-01-15 15:25:18"
    ["modified"]=>
    string(19) "2026-01-15 15:25:18"
    ["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_overridden_res"]=>
  string(1) "3"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(1) {
  [0]=>
  string(14) "United Kingdom"
}


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


products
array(4) {
  [0]=>
  string(6) "invega"
  [1]=>
  string(9) "risperdal"
  [2]=>
  string(16) "risperdal-consta"
  [3]=>
  string(15) "invega-sustenna"
}


num of trials
array(1) {
  [0]=>
  string(2) "40"
}


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