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  ["project_title"]=>
  string(81) "Identifying predictors of symptom stability in absence of antipsychotic treatment"
  ["project_narrative_summary"]=>
  string(652) "Not all individuals with schizophrenia benefit from antipsychotic drugs.  The majority experience immediate and long-term adverse effects raising risk/benefit dilemmas for patients and for  prescribers. An alternative treatment scheme proposes a targeted or intermittent treatment approach, by which antipsychotic drugs are administered upon psychosis exacerbation and discontinued upon remission or stabilization, and renewed treatment in case of symptoms recurrence. However, there are currently no reliable biological or phenomenological markers able to predict which patients will maintain symptom stability in absence of antipsychotic treatment.  "
  ["project_learn_source"]=>
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  array(7) {
    ["first_name"]=>
    string(8) "Jonathan"
    ["last_name"]=>
    string(10) "Rabinowitz"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(19) "Bar-Ilan University"
    ["email"]=>
    string(29) "jonathan.rabinowitz@biu.ac.il"
    ["state_or_province"]=>
    string(8) "- None -"
    ["country"]=>
    string(6) "Israel"
  }
  ["project_key_personnel"]=>
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      ["p_pers_f_name"]=>
      string(6) "Helene"
      ["p_pers_l_name"]=>
      string(6) "Speyer"
      ["p_pers_degree"]=>
      string(7) "MD, PhD"
      ["p_pers_pr_affil"]=>
      string(31) "Copenhagen University Hospital "
      ["p_pers_scop_id"]=>
      string(12) " 56685782700"
      ["requires_data_access"]=>
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    ["value"]=>
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    ["label"]=>
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  ["property_scientific_abstract"]=>
  string(1548) "Background:  Not all individuals with schizophrenia benefit from the antipsychotic drugs.  The majority experience immediate and long-term adverse effects raising risk/benefit dilemmas for the patients and for the prescribers. An alternative treatment scheme proposes a targeted or intermittent treatment approach, by which antipsychotic drugs are administered upon psychosis exacerbation and discontinued upon remission or stabilization, and renewed treatment in case of symptoms recurrence. However, there are currently no reliable biological or phenomenological markers able to predict which patients will maintain symptoms stability in absence of antipsychotic treatment.  

Objective: Identify markers that can help predict which patients maintain symptom stability in absence of antipsychotic treatment.  

Study Design:  Data from the requested studies will be merged into an integrated database to facilitate meta-analysis of individual participant data. 

Participants:  Six placebo-controlled antipsychotic trials with a double-blind phase of at least 8 months.  

Primary and Secondary Outcome Measure(s); Time to: (1) relapse as measured on the PANSS, (2) discontinuation or (3) hospitalization.  Secondary outcome measures time to relapse as measured on the CGI-S.  

Statistical Analysis: Time to drop-out, relapse or hospitalization will be analyzed using Cox-regression.  Predictor variables to be tested included routinely collected symptom, demographic and clinical variables in antipsychotic trials."
  ["project_brief_bg"]=>
  string(1367) "Current pharmacological treatment of schizophrenia employs drugs that interfere with dopamine neurotransmission, aiming to suppress acute exacerbation of psychosis and maintenance treatment to reduce the risk of psychosis recurrence.  However, not all individuals who meet DSM criteria for schizophrenia benefit from the antipsychotic drugs and the majority experience immediate and long-term adverse effects raising risk/benefit dilemmas for the patients and for the prescribers. An alternative treatment scheme proposes a targeted or intermittent treatment approach, by which antipsychotic drugs are administered upon psychosis exacerbation and discontinued upon remission or stabilization, and renewed treatment in case of symptoms recurrence. However, there are currently no reliable biological or phenomenological markers able to predict which patients will maintain symptoms stability in absence of antipsychotic treatment and which patients are likely to experience symptoms worsening shortly after treatment discontinuation. A recent study has suggested that patients who experience high level of negative symptoms and low levels of symptoms related to agitation may be able to maintain symptoms stability and avoid hospitalization in absence of drugs which directly interfere with dopamine neurotransmission for at least 12 months (Rabinowitz et al, 2023).  "
  ["project_specific_aims"]=>
  string(494) "The aim of this project is to identify markers that can help predict which patients maintain symptoms stability in absence of antipsychotic treatment.  Based on previous work, I hypothesize that trial participants with predominantly negative symptoms and low agitation at baseline will sustain without relapse longer than patients without this symptom profile.  Attempts will be made to develop and test other symptom typologies that predict stability in the absence of antipsychotic treatment."
  ["project_study_design"]=>
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    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
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  ["project_purposes"]=>
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    [0]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(56) "participant_level_data_meta_analysis_from_yoda_and_other"
      ["label"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
    }
    [2]=>
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      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
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  }
  ["project_research_methods"]=>
  string(480) "1. None.

2. Other studies from VIVILI

>Olanzapine Relapse Prevention Versus Placebo  https://benmeg.com/archives/rxarchives.org/uploads/2/4/4/6/24466638/1960_online.pdf

IDs: F1D-MC-HGGI



>Intramuscular Depot Formulation of Aripiprazole as Maintenance Treatment in Patients With Schizophrenia (ASPIRE)

https://clinicaltrials.gov/study/NCT00705783



3. We plan to pool the studies in VIVILI unless it can be done in YODA."
  ["project_main_outcome_measure"]=>
  string(177) "Primary outcome measures:  Change on the PANSS total score, time to discontinuation and time to hospitalization.  Secondary measures to be includes with be CGI-S, CGI-C and PSP."
  ["project_main_predictor_indep"]=>
  string(337) "The main independent variables will be symptomatology at baseline as measured on the PANSS items, subscales and total score, CGI-S, PSP and medical history (e.g., age of first illness, number of hospitalizations and medication history), smoking, tardive dyskinesia, akathisia, demographic variables (age, sex, race, ethnicity, region).  "
  ["project_other_variables_interest"]=>
  string(333) "These variables will be used interchangeably as independent variables and as other variables used to adjust in multivariate models: and medical history (e.g., age of first illness, number of hospitalizations and medication history), smoking, tardive dyskinesia, akathisia, demographic variables (age, sex, race, ethnicity, region).  "
  ["project_stat_analysis_plan"]=>
  string(3808) "We will conduct a one-stage individual patient data (IPD) meta-analysis as the primary analytic framework (Campbell H, Maciel D, Chan K, et al, 2025). This approach will allow us to model all patient-level data simultaneously across the included trials, accounting for study-level heterogeneity through random effects. For the time-to-event outcome, we will implement a Cox proportional hazards model within the one-stage framework. Hazard ratios (HRs) with 95% confidence intervals (Cis) will be reported. Prognostic models will be used to identify baseline characteristics associated with outcomes regardless of treatment. We will fit a Cox proportional hazards models including baseline covariates as main effects and stratify by study to account for between-trial differences. Hazard ratios (HRs) with 95% confidence intervals (CIs) will be reported to quantify the association between each baseline factor and the risk of the event. Predictive models will be used to examine whether baseline characteristics modify the effect of treatment. These models will include interaction terms between treatment and the baseline covariates (treatment × covariate), in addition to the main effects. Stratification by study will account for differences in trial design. Significant interaction terms indicate that the effect of treatment varies according to the baseline characteristic. Subgroup-specific HRs will be reported for interpretation. Cox model development will follow a structured approach. Initially, univariable associations between each candidate predictor and the outcome will be examined. Predictors showing evidence of association (p < 0.1) will be considered for inclusion in multivariable models. A global multivariable Cox model will then be built, incorporating all selected variables, with stepwise evaluation to retain variables that contribute meaningfully to model fit. In the final analysis, we will use a network approach to connect patients and their characteristics across all included studies. This allows us to examine how different factors—such as symptom profiles, demographics, and prior treatment—interact to influence outcomes. By considering both direct and indirect relationships, the network analysis can help to identify patterns of symptom stability without medication more accurately than looking at each study or factor separately."



Predictor variables to be tested include symptomatology at baseline as measured on the PANSS items, subscales and total score, CGI-S, PSP and medical history (e.g., age of first illness, number of hospitalizations and medication history), smoking, tardive dyskinesia, akathisia, demographic variables (age, sex, race, ethnicity, region). To test the hypothesis that patients who manifest primarily negative symptoms are less likely to relapse on placebo dichotomous variables of meeting criteria for negative symptoms will be created using known negative symptom population groupings (Rabinowitz et al 2013; Rabinowitz et al, 2023) and compared to the effect of this grouping variable for patients on active treatment. This will be done by testing interaction of placebo vs. active treatment and yes/no negative symptom patient and separate models for placebo and active treatment will also be run and effects of the negative symptom grouping variable will be compared. To account for study differences study name will be included in the analysis as a control variable and we will also examine the interaction of study name and outcomes.  We will also carefully compare the inclusion criteria and patient characteristics before pooling studies. Missing data due to dropout will be handled by censoring the patient from time of discontinuation.  Thus, all patients will be included and no data will be imputed. "
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      ["label"]=>
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  ["project_timeline"]=>
  string(238) "Anticipated start date: April 15, 2025

Analysis completion date: Sept 15, 2025

Date manuscript drafted and first submitted for publication:  Nov. 15, 2025

Date results reported back to the YODA Project:  Nov. 15, 2025"
  ["project_dissemination_plan"]=>
  string(129) "Conference poster at schizophrenia conference on early findings and journal article in specialty journal upon project completion."
  ["project_bibliography"]=>
  string(2345) "
Leucht S, Tardy M, Komossa K, Heres S, Kissling W, Salanti G, Davis JM. Antipsychotic drugs versus placebo for relapse prevention in schizophrenia: a systematic review and meta-analysis. Lancet. 2012 Jun 2;379(9831):2063-71. doi: 10.1016/S0140-6736(12)60239-6. Epub 2012 May 3. PMID: 22560607.
Rabinowitz J, Staner C, Saoud J, Weiser M, Kuchibhatla R, Davidson M, Harvey PD, Luthringer R. Long-term effects of Roluperidone on negative symptoms of schizophrenia. Schizophr Res. 2023 May;255:9-13. doi: 10.1016/j.schres.2023.03.028. Epub 2023 Mar 16. PMID: 36933291.
Rabinowitz J, Levine SZ, Barkai O, Davidov O. Dropout rates in randomized clinical trials of antipsychotics: a meta-analysis comparing first- and second-generation drugs and an examination of the role of trial design features. Schizophr Bull. 2009 Jul;35(4):775-88. doi: 10.1093/schbul/sbn005. Epub 2008 Feb 26. PMID: 18303093; PMCID: PMC2696366.
Siafis S, Brandt L, McCutcheon RA, Gutwinski S, Schneider-Thoma J, Bighelli I, Kane JM, Arango C, Kahn RS, Fleischhacker WW, McGorry P, Carpenter WT, Falkai P, Hasan A, Marder SR, Schooler N, Engel RR, Honer WG, Buchanan RW, Davidson M, Weiser M, Priller J, Davis JM, Howes OD, Correll CU, Leucht S. Relapse in clinically stable adult patients with schizophrenia or schizoaffective disorder: evidence-based criteria derived by equipercentile linking and diagnostic test accuracy meta-analysis. Lancet Psychiatry. 2024 Jan;11(1):36-46. doi: 10.1016/S2215-0366(23)00364-4. Epub 2023 Nov 30. PMID: 38043562.
Rabinowitz J, Werbeloff N, Caers I, Mandel FS, Stauffer V, Menard F, Kinon BJ, Kapur S. Negative symptoms in schizophrenia–the remarkable impact of inclusion definitions in clinical trials and their consequences. Schizophr Res. 2013 Nov;150(2-3):334-8. doi: 10.1016/j.schres.2013.06.023. Epub 2013 Jun 29. PMID: 23815975.
Georgios Schoretsanitis, John M Kane, Christoph U Correll, Jose M Rubio, Predictors of Lack of Relapse After Random Discontinuation of Oral and Long-acting Injectable Antipsychotics in Clinically Stabilized Patients with Schizophrenia: A Re-analysis of Individual Participant Data, Schizophrenia Bulletin, Volume 48, Issue 2, March 2022, Pages 296–306, https://doi.org/10.1093/schbul/sbab091
"
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    string(47) "yoda-project-due-diligence-assessment-2025-0120"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(16606)
    ["date"]=>
    string(19) "2025-09-15 16:19:47"
    ["modified"]=>
    string(19) "2025-09-15 16:19:47"
    ["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(17906)
    ["id"]=>
    int(17906)
    ["title"]=>
    string(44) "YODA Project Protocol 2025-0120 - 2025-09-02"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0120-2025-09-02.pdf"
    ["filesize"]=>
    int(178642)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/02/YODA-Project-Protocol-2025-0120-2025-09-02.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0120/yoda-project-protocol-2025-0120-2025-09-02/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0120-2025-09-02"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(16606)
    ["date"]=>
    string(19) "2025-09-15 16:20:21"
    ["modified"]=>
    string(19) "2025-09-15 16:20:21"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
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    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(17907)
    ["id"]=>
    int(17907)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0120_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0120_site.pdf"
    ["filesize"]=>
    int(1315503)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/02/YODA-Project-Review-2025-0120_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0120/yoda-project-review-2025-0120_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
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    ["status"]=>
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    ["uploaded_to"]=>
    int(16606)
    ["date"]=>
    string(19) "2025-09-15 16:20:44"
    ["modified"]=>
    string(19) "2025-09-15 16:20:44"
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    ["mime_type"]=>
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    ["type"]=>
    string(11) "application"
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    string(3) "pdf"
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  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["request_overridden_res"]=>
  string(1) "3"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(2) {
  [0]=>
  string(6) "invega"
  [1]=>
  string(15) "invega-sustenna"
}


num of trials
array(1) {
  [0]=>
  string(1) "4"
}


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