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In this proposal, we extend this across schizophrenia (58 trials, 3 endpoint families), psoriasis (29 trials, 2 endpoint families), and prostate cancer (16 trials, 2 endpoints) using broader datasets (a total of 103 trials) and model classes (ranging from classical regression-based to deep learning), with strict external validation.  



[1] Chekroud AM, et al. Illusory eneralizability of clinical prediction models. Science 2024;383:16"
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      string(20) "University of Geneva"
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      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Jiawei"
      ["p_pers_l_name"]=>
      string(2) "He"
      ["p_pers_degree"]=>
      string(3) "MSc"
      ["p_pers_pr_affil"]=>
      string(20) "University of Geneva"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
  }
  ["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(1664) "Background: Prior work using YODA data suggested that machine learning models for clinical outcome prediction may perform well within a trial but generalize poorly to unseen trials. However, this evidence was limited to one condition, few trials, and a restricted set of modeling approaches.



Objective: To evaluate cross-trial generalizability of clinical prediction models across multiple conditions, endpoint families, and model classes.



Design: Participant-level analysis of YODA CTs in schizophrenia (3 endpoints), psoriasis (2 endpoints), and prostate cancer (2 endpoints). Analyses will be conducted separately within each family.



Participants: Adult participants with available baseline data and relevant outcome measurements. Participants with insufficient baseline for a given analysis will be excluded.



Outcomes: The primary study outcome is model performance on unseen trials under leave-one-trial-out validation. Clinical target endpoints include psychiatric symptom response, relapse/recurrence, skin clearance/PASI-IGA response, psoriatic arthritis response, survival/progression, and biomarker/tumor response, depending on condition and endpoint family.



Statistical Analysis: We will harmonize shared baseline variables within each condition and endpoint family, train multiple prediction models, and evaluate transportability using LOTO validation. Models will include penalized regression, tree-based models, and neural-network-based models. Performance will be assessed using discrimination, balanced accuracy, calibration, and survival-specific metrics where applicable."
  ["project_brief_bg"]=>
  string(2419) "Clinical prediction models are increasingly proposed as tools to support treatment selection, risk stratification, and patient-level decision-making. However, for such models to be clinically useful, they must generalize beyond the specific trial or dataset in which they were developed. A recent study using YODA clinical trial data found that machine learning models predicting treatment outcomes in schizophrenia performed well within individual trials but poorly when tested on independent unseen trials. This raises an important concern: clinical prediction models may be more context-dependent than commonly assumed.



However, the existing evidence is still limited. The prior work focused on antipsychotic treatment trials in schizophrenia, used a relatively small number of trials, and relied on a limited set of modeling strategies. It remains unclear whether poor cross-trial generalizability is a general property of clinical prediction models, or whether it depends on the disease area, endpoint type, sample size, trial heterogeneity, and model class.



This project will extend the prior work by evaluating cross-trial generalizability across three therapeutic areas: schizophrenia, psoriasis, and prostate cancer. These conditions provide distinct clinical settings, endpoint structures, and trial populations. Analyses will be conducted separately within condition-specific endpoint families to avoid inappropriate pooling of clinically heterogeneous data. The study will compare classical statistical models, tree-based/boosting models, and neural-network-based tabular models under strict external validation.



The proposed work will materially enhance scientific knowledge by clarifying when clinical prediction models do or do not transport across independent trials. It will also provide practical guidance for future clinical machine learning studies by identifying which validation designs, endpoint families, and modeling approaches provide more reliable estimates of generalizability. This is directly relevant to clinical trial methodology, evidence generation, and the safe development of prediction models intended to inform clinical and public health decisions.



Reference: Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, et al. Illusory generalizability of clinical prediction models. Science. 2024;383:164–167."
  ["project_specific_aims"]=>
  string(1014) "Aim 1: Quantify the cross-trial generalizability of clinical prediction models within each condition and endpoint family.

Hypothesis: Models will show lower performance on unseen trials than under within-trial validation, but the magnitude of this drop will vary by condition and endpoint family.



Aim 2: Evaluate whether generalizability differs across therapeutic areas and clinical endpoint families.

Hypothesis: Transportability will depend on the clinical setting and endpoint type, with more harmonized endpoint families expected to show better cross-trial performance.



Aim 3: Compare multiple model classes for cross-trial prediction.

Hypothesis: More flexible model classes, including tree-based and neural-network-based models, may improve performance in some settings, but gains will depend on sample size, endpoint harmonization, and trial heterogeneity. The primary objective is empirical evaluation rather than assuming superiority of any model class."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(8) "meth_res"
    ["label"]=>
    string(23) "Methodological research"
  }
  ["project_purposes"]=>
  array(5) {
    [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(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
    [4]=>
    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(1323) "The requested analytical data source is individual participant-level data from YODA clinical trials in schizophrenia, psoriasis, and prostate cancer. ClinicalTrials.gov and YODA trial metadata were used only to identify and categorize eligible trials; no non-YODA participant-level data will be pooled with YODA data.



Participants will be included if they are adult trial participants from the requested studies and have sufficient baseline data and at least one relevant outcome measurement for the condition-specific endpoint family being analyzed. Analyses will be conducted separately within each condition and endpoint family.



Participants will be excluded from a specific analysis if they lack the relevant outcome measurement, have insufficient baseline covariate data after harmonization, or belong to a trial/endpoint family not included in that specific analysis. No additional demographic exclusions will be applied unless required by endpoint-specific harmonization.



Trial-level inclusion criteria are: randomized interventional trials with available IPD, sufficient baseline variables, and endpoints that can be harmonized within condition-specific endpoint families. Trials with unclear or sparse endpoint categories were excluded from the main retained inventory."
  ["project_main_outcome_measure"]=>
  string(1517) "The primary study outcome is prediction model performance on unseen trials under leave-one-trial-out validation within each condition and endpoint family. Primary performance measures will include discrimination and classification performance, such as ROC-AUC and balanced accuracy, with calibration assessed using metrics such as the Brier score. For time-to-event endpoints, survival-specific metrics such as concordance index or time-dependent AUC will be used where applicable.



The clinical target endpoints to be predicted will be defined within condition-specific endpoint families:



- Schizophrenia: psychiatric symptom response endpoints, relapse/recurrence endpoints, and safety/tolerability endpoints.

- Psoriasis: skin clearance or disease activity endpoints, including PASI/IGA-type responses, and psoriatic arthritis response endpoints where applicable.

- Prostate cancer: survival or disease progression endpoints, and biomarker/tumor response endpoints.



Secondary outcome measures will include within-trial cross-validation performance, pooled multi-trial validation performance, calibration performance, and differences in model performance across model classes, conditions, and endpoint families.



If final endpoint definitions differ from the above categories after review of the available IPD and supporting documentation, such changes will be documented before analysis and reported transparently in any resulting manuscript."
  ["project_main_predictor_indep"]=>
  string(1199) "This is a clinical prediction and model-transportability study rather than a causal analysis of a single independent variable. The main predictor set will consist of baseline participant-level variables available before outcome assessment and harmonizable within each condition and endpoint family.



Predictors may include demographic variables, baseline disease severity measures, prior disease or treatment history, baseline clinical assessments, laboratory values, and treatment assignment where appropriate and available. Variables will be categorized according to their original trial definitions and harmonized across trials when comparable definitions are available. Continuous variables will generally be retained as continuous predictors after standardization; categorical variables will be encoded using harmonized categories.



The main methodological variable of interest is the validation setting, especially whether model performance is evaluated within the same trial or on an independent unseen trial. The primary objective is to assess prediction model transportability across trials, not to estimate the independent causal effect of any single predictor."
  ["project_other_variables_interest"]=>
  string(1302) "Other variables of interest will be used to characterize the study samples, assess trial heterogeneity, and support model evaluation. These may include trial identifier, condition, endpoint family, treatment arm, trial design characteristics, follow-up timepoint, and availability/missingness indicators for key baseline variables.



Participant-level descriptive variables will include demographics, baseline disease severity, prior treatment or disease history, baseline clinical assessments, and laboratory or biomarker measures where available. These variables will be defined according to the original trial data dictionaries and harmonized within each condition and endpoint family when comparable definitions exist.



Trial-level variables, such as trial identifier and endpoint family, will not be used to pool clinically incompatible trials. Instead, they will be used to define analysis strata, support leave-one-trial-out validation, and describe sources of heterogeneity in model performance.



Where appropriate, treatment assignment and follow-up duration will be included as design/context variables or used for stratified analyses. Missingness indicators may be used to describe data availability and, if appropriate, support sensitivity analyses."
  ["project_stat_analysis_plan"]=>
  string(2792) "Analyses will be conducted separately within each condition and endpoint family. We will not pool clinically incompatible conditions or endpoint types into a single prediction task.



First, we will summarize the available trials and participant samples using descriptive statistics. Participant characteristics, baseline disease severity, treatment assignment, endpoint availability, and missingness patterns will be described within each condition and endpoint family. Trial-level characteristics and sample sizes will also be summarized.



Second, we will harmonize baseline variables and endpoints across trials within each condition and endpoint family. Primary analyses will use variables that are sufficiently comparable across trials. Continuous variables will be standardized, categorical variables will be harmonized where possible, and missing data will be handled using imputation procedures fit only within training data to avoid leakage into held-out trials.



Third, we will train and evaluate multiple prediction model classes, including penalized regression models, tree-based/boosting models, and neural-network-based tabular models where feasible within the secure platform. Model complexity and hyperparameters will be selected using internal cross-validation within the training data only.



The primary validation strategy will be leave-one-trial-out validation. For each analysis family, models will be trained on all eligible trials except one and tested on the held-out trial. This process will be repeated so that each eligible trial serves as the external test trial. This design estimates model transportability to independent unseen trials.



Secondary analyses will include within-trial cross-validation, paired cross-trial validation where feasible, and pooled multi-trial training with held-out trial testing. These analyses will allow comparison of optimistic within-trial performance with external cross-trial performance.



For binary endpoints, performance will be assessed using ROC-AUC, balanced accuracy, sensitivity, specificity, and calibration metrics such as the Brier score. For continuous endpoints, performance will be assessed using error-based metrics such as RMSE or MAE. For time-to-event endpoints, Kaplan-Meier summaries, Cox modeling, concordance index, and time-dependent AUC may be used where appropriate.



Model performance will be compared across model classes, endpoint families, and conditions. Sensitivity analyses will evaluate the impact of variable availability, missing data handling, endpoint definitions, and trial heterogeneity. No causal treatment-effect claims will be made unless specifically supported by the design and analysis."
  ["project_software_used"]=>
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    [0]=>
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      ["value"]=>
      string(6) "python"
      ["label"]=>
      string(6) "Python"
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  }
  ["project_timeline"]=>
  string(911) "The project is planned within the 12-month YODA data access period.



Months 1–2: Data access setup, review of data dictionaries and supporting documentation, confirmation of endpoint definitions, and finalization of analysis cohorts.



Months 3–4: Data harmonization across trials within each condition and endpoint family, baseline variable mapping, missingness assessment, and preparation of analysis datasets.



Months 5–8: Model development and validation, including leave-one-trial-out validation, within-trial validation, and model comparison across endpoint families.



Months 9–10: Sensitivity analyses, calibration analyses, survival-specific analyses where applicable, and robustness checks.



Months 11–12: Manuscript drafting, internal review, first manuscript submission, and reporting of results back to the YODA Project."
  ["project_dissemination_plan"]=>
  string(220) "Results will be submitted to peer-reviewed journals within the digital health and medical informatics communities, such as Nature Digital Medicine, Lancet Digital Health, JAMIA, IEEE JBHI, JMIR, PLoS Digital Health, etc."
  ["project_bibliography"]=>
  string(221) "
1. Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, et al. Illusory generalizability of clinical prediction models. Science 2024;383:164–167.
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data partner
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pi country
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pi affil
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products
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  [1]=>
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  [3]=>
  string(16) "risperdal-consta"
  [4]=>
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  [8]=>
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  [9]=>
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  [10]=>
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num of trials
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res
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