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  ["project_title"]=>
  string(118) "Predicting Next Visit Outcomes in Antipsychotic Clinical Trials: A Data-Driven Approach to Individualized Forecasting "
  ["project_narrative_summary"]=>
  string(860) "For this analysis, we will include only randomized clinical trials of antipsychotics that lasted at least six months and had monthly or more frequent assessments of symptom severity. Individual predictive models are emerging in psychiatry and a very relevant actionable individual prediction is for outcomes of interest at the next visit based on the information learned about a given patient so far. We propose to use visit-by-visit data to build predictive models that forecast the next visit’s outcomes for each patient. By incorporating cumulative information up to each assessment, this framework aims to maiximize the information that can be used to make predictions and present it in an actionable way. The project’s deliverable will be a validated modeling strategy for individualized prediction of visit to visit outcomes in schizophrenia.

"
  ["project_learn_source"]=>
  string(9) "colleague"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(4) "Jose"
    ["last_name"]=>
    string(5) "Rubio"
    ["degree"]=>
    string(2) "MD"
    ["primary_affiliation"]=>
    string(58) "Northwell Health Feinstein Institutes for Medical Research"
    ["email"]=>
    string(22) "JRubio13@northwell.edu"
    ["state_or_province"]=>
    string(2) "NY"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
  array(2) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(4) "Joey"
      ["p_pers_l_name"]=>
      string(5) "Zhang"
      ["p_pers_degree"]=>
      string(20) "Bachelors of Science"
      ["p_pers_pr_affil"]=>
      string(22) "Stony Brook University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Mehrdad"
      ["p_pers_l_name"]=>
      string(5) "Sadri"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
      string(58) "Northwell Health Feinstein Institutes for Medical Research"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["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(1393) "Background: In long-term studies of schizophrenia, researchers usually look at whether patients get better overall by the end of the trial. However, clinicians need more immediate predictions on whether a given outcome will occur before the next visit, so that they can modify interventions accordingly. We argue that these predictions may get better as more data about the trajectory for a given patient accumulates.

Objective: We want to test whether we can use detailed visit-by-visit data to predict what a patient’s symptoms will look like at the next visit.

Study Design: We will use clinical trial data where patients with schizophrenia or schizoaffective disorder were treated with antipsychotics for at least six months, and where there were monthly check-ins or more frequent assessments. We will build models that use all the information collected up to each visit to predict what will happen at the following visit.

Participants: People in schizophrenia clinical trials that meet the criteria above.

Primary Outcome: Study defined relapse, treatment disengagement, emergence of side effects.

Statistical Analysis: At each visit t, we will predict next-visit (t+1) occurrence of any of three events - study defined relapse, treatment disengagement, or a new adverse event - using only information available up to t. (See more in supp. materials)"
  ["project_brief_bg"]=>
  string(689) "While many patients initially respond to antipsychotics, a subset experiences progressive

symptom worsening despite continued treatment. Current analytic strategies emphasize

overall perceived response and neglect visit-level dynamics. Our inclusion criteria (≥6-

month duration, monthly or more frequent assessments) ensure sufficient longitudinal

density to study trajectories. Building prediction models that forecast outcomes at the next

visit mirrors real-world clinical decision making and can identify high-risk patients earlier.

This work will refine how long-term trial data are analyzed and inform adaptive treatment

strategies."
  ["project_specific_aims"]=>
  string(184) "Specific Aims

1. Develop predictive models that use cumulative visit-level information to forecast next-visit symptom outcomes.

2. Identify predictors of such predictions"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(3) {
    [0]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(37) "participant_level_data_only_from_yoda"
      ["label"]=>
      string(51) "Meta-analysis using only data from the YODA Project"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(419) "Study Design

• Data Source: Patient-level data from schizophrenia trials available via the YODA Project.

• Inclusion Criteria:

o Trial duration ≥6 months.

o At least monthly study visits.

o Participants with schizophrenia or schizoaffective disorder randomized to active antipsychotic treatment.



• Exclusion Criteria: None beyond failing inclusion requirements."
  ["project_main_outcome_measure"]=>
  string(168) "Primary and Secondary Outcomes

• Primary: Study defined relapse by next visit

• Secondary: treatment disengagement by next visit, new AE by next visit"
  ["project_main_predictor_indep"]=>
  string(143) "Predictors

• Baseline features: All available information (demographic, medical and psychiatric illness, baseline AE, psychopathology)"
  ["project_other_variables_interest"]=>
  string(3) "n/a"
  ["project_stat_analysis_plan"]=>
  string(2868) "1. Preprocessing

We’ll line up all trials on the same timeline (days on study) and include only trials that last

at least six months with monthly or more frequent visits. Within each training split (to avoid

information leaks), we will fill in missing values, standardize numeric variables to the same

scale, and build “at-this-visit (t)” features using only information collected up to that visit:

last PANSS score, change since baseline and since the prior visit, short short-term trend

over the last 2–3 visits, a person-specific slope and fitted level estimated from their own

history so far, symptom volatility (moving average and rolling variability), time on treatment

and dose, adherence flags, recent adverse events, and site/trial indicators.

2. Modeling approaches

a) Linear mixed-effects (primary): logistic models that predict whether an event will happen

at the next visit (t+1), with participant-level random effects (and optional site effects) to



account for repeated measures.

b) Autoregressive models: include the most recent outcome level(s) plus other features to

capture the “from-visit-to-visit” dependence. (We will also frame this as a discrete-time

survival model to estimate interval risk between visits.)

c) Machine learning: elastic-net logistic regression (for robust feature selection) and a

gradient-boosted tree model (for non-linear patterns and interactions).

3. Validation

We will test generalizability by leaving one site out each time: train on all other sites, test

on the held-out site, and rotate through all sites. (Where relevant, we will also leave one

trial out.) Inside every training split, all preprocessing and hyperparameter tuning happen

only on the training data. We also enforce a rolling-origin rule—predictions for t+1 can use

data only up to t. If events are rare, we will use class weights so the model does not ignore

them.

4. Stratification

After we get predicted next-visit risk for each person, we will sort everyone and split them

into three groups: Low, Medium, and High risk. If any group is too small (less than 10% of

participants), we will merge it with the nearest group to keep results stable. We will then

check that these groups truly differ over time by testing a group × time interaction and by

plotting cumulative event rates for each group.

5. Feature importance

For regression models, we will report standardized coefficients (with confidence intervals)

to show direction and strength. For tree-based models, we will use SHAP values to show

which variables most influence the predicted next-visit event risk in an interpretable way."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(290) "We will need for the analyses both R (through RStudio) and Python (through Jupyter Lab or Jupyter Notebook).

We plan to start as soon as we are granted access to the data. We anticipate 3 months for data preprocessing, 6 for analyses, and 3 for manuscript preparation and submission."
  ["project_dissemination_plan"]=>
  string(269) "Findings will be submitted to high-impact psychiatry and psychopharmacology journals and presented at national conferences. Code and analytic pipelines will be shared in line with YODA’s open-science principles.



Lancet Psychiatry as potential  journal."
  ["project_bibliography"]=>
  string(706) "
James, Gareth, et al.  “An introduction to statistical learning: With applications in Python.” Cham: Springer International Publishing, 2023. 15-67.
Poldrack, Russel A., Grace Huckins, and Gael Varoquaux. “Establishment of best practices for evidence for prediction: a review.” JAMA psychiatry 77.5 (2020): 534-540.
Rubio JM, Schoretsanitis G, John M, Tiihonen J, Taipale H, Guinart D, Malhotra AK, Correll CU, and Kane JM. “Psychosis relapse during treatment with long-acting injectable antipsychotics in individuals with schizophrenia-spectrum disorders: an individual participant data meta-analysis.” Lancet Psychiatry. 2020 Sep; 7(9): 749-761.
"
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        ["filesize"]=>
        int(225281)
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        string(75) "https://yoda.yale.edu/wp-content/uploads/2025/10/Statistical-Analysis-.docx"
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        ["date"]=>
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  ["data_use_agreement_training"]=>
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data partner
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}


pi country
array(0) {
}


pi affil
array(0) {
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products
array(3) {
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}


num of trials
array(1) {
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}


res
array(1) {
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}