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  string(537) "We propose to evaluate whether synthetic individual participant data (IPD) reconstructed from published randomized trial information can be used to extend trial findings to real-world target populations. Using YODA trials with available original IPD as benchmarks, we will compare transported treatment effects obtained from synthetic versus original trial data. We will also assess whether adding external information on relationships among patient characteristics improves the accuracy of synthetic-IPD-based transportability analyses."
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(6) "Liyuan"
    ["last_name"]=>
    string(3) "Tao"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(32) "Peking University Third Hospital"
    ["email"]=>
    string(21) "liyuantao@bjmu.edu.cn"
    ["state_or_province"]=>
    string(7) "Beijing"
    ["country"]=>
    string(5) "China"
  }
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      ["p_pers_f_name"]=>
      string(8) "Xiaoying"
      ["p_pers_l_name"]=>
      string(5) "Zhong"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(38) "Beijing University of Chinese Medicine"
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      ["p_pers_f_name"]=>
      string(8) "Jiaojiao"
      ["p_pers_l_name"]=>
      string(4) "Liao"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
      string(32) "Peking University Third Hospital"
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  ["project_ext_grants"]=>
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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(1395) "Background: Generalizability and transportability methods commonly require trial individual participant data (IPD) to characterize covariate distributions and treatment-effect heterogeneity. When IPD are unavailable, only aggregate information may be accessible.

Objective: To develop and empirically validate a synthetic-IPD framework for extending randomized trial inferences to target populations.

Study Design: Methodological proof-of-concept study using randomized clinical trials available through the YODA Project.

Participants: Randomized participants from selected YODA trials in type 2 diabetes, obesity, and related metabolic conditions will be included. 

Primary and Secondary Outcome Measure(s): The primary outcome is the difference between transported treatment-effect estimates obtained using synthetic versus original trial IPD. Secondary outcomes include absolute and relative bias, agreement in effect direction and interpretation, and performance across reconstruction strategies and transportability estimators.

Statistical Analysis: We will compare synthetic IPD reconstructed from published marginal information alone versus reconstruction additionally informed by external covariate dependence structures. Original YODA IPD will serve as the empirical benchmark. Standardization, weighting, and doubly robust estimators will be applied."
  ["project_brief_bg"]=>
  string(2764) "Randomized clinical trials (RCTs) provide internally valid estimates of treatment effects, but trial participants may differ from the populations in which treatments are ultimately used. Generalizability and transportability methods address this problem by combining trial data with information from a target population.[1,2,3,4,5] Most existing approaches require individual participant data (IPD) from the trial to characterize baseline covariates, model treatment-effect heterogeneity, and adjust for differences between trial and target populations using standardization, weighting, calibration, or doubly robust methods.[1,6,7,8]

In practice, however, IPD from many completed RCTs are unavailable. Researchers often have access only to published aggregate information, including sample size, baseline characteristics, treatment-group outcomes, marginal covariate distributions, and selected subgroup effects. This limits the application of established IPD-based transportability methods to a large body of published trial evidence.[9,10]

One potential solution is to reconstruct synthetic IPD from publicly available trial information. For transportability analyses, however, synthetic data must reproduce more than the overall trial characteristics or average treatment effect. Valid transport also depends on the distribution of treatment-effect modifiers and their joint relationships.[8] Published reports commonly provide only marginal summaries and may not contain sufficient information to recover these multivariable structures.[10]

We propose a methodological proof-of-concept study using original IPD from selected YODA RCTs as an empirical benchmark.[11] For each trial, we will first estimate the target-population treatment effect using the original IPD. We will then reconstruct synthetic IPD using information that would typically be available from trial publications and repeat the same transportability analyses.[2]

Two reconstruction strategies will be compared: one based on published marginal information alone and another additionally informed by external data on the joint distribution of relevant covariates.[8] Synthetic-IPD-based transported treatment effects will be compared with estimates obtained from the original YODA IPD across multiple trials and transportability estimators.

This study will provide empirical evidence on whether publication-constrained synthetic IPD can support reliable generalizability and transportability analyses,[5,9] identify when published marginal information is insufficient, and assess whether external covariate dependence information improves reconstruction accuracy. The findings may expand the use of completed RCT evidence when original IPD are unavailable."
  ["project_specific_aims"]=>
  string(594) "Aim 1. Develop a publication-constrained approach for reconstructing synthetic trial IPD from aggregate information commonly reported in randomized trial publications.

Aim 2. Compare two synthetic-IPD reconstruction strategies: reconstruction based only on published marginal information and reconstruction additionally informed by external covariate dependence structures.

Aim 3. Evaluate the accuracy of synthetic-IPD-based transportability analyses by comparing transported treatment effects with estimates obtained using the original YODA trial IPD as the empirical benchmark."
  ["project_study_design"]=>
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    string(8) "meth_res"
    ["label"]=>
    string(23) "Methodological research"
  }
  ["project_purposes"]=>
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    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(69) "confirm_or_validate previously_conducted_research_on_treatment_safety"
      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
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  ["project_research_methods"]=>
  string(1468) "We will use IPD from randomized clinical trials available through the YODA Project, including trials in type 2 diabetes, obesity, hypertension, and related metabolic conditions.

Inclusion criteria: For each YODA trial, we will include all randomized participants who were included in the original trial population and who have a valid randomized treatment assignment. We will retain the demographic and clinical eligibility criteria originally specified by each trial and will not impose additional restrictions based on age, sex, race/ethnicity, disease severity, or comorbidities. Where possible, the primary analysis will follow the original intention-to-treat population.

Exclusion criteria: Participants with missing or invalid treatment assignment or without sufficient outcome information to reproduce the trial treatment effect will be excluded. 

Original YODA IPD will be used to reproduce trial treatment effects and provide the empirical benchmark for synthetic-IPD analyses.

External target-population data will be obtained from publicly available NHANES data. Trial-specific target populations will be defined according to the major treatment-eligibility characteristics that can be harmonized between each trial and NHANES. NHANES IPD will not be pooled with YODA IPD. External data will be analyzed separately to obtain target-population covariate distributions and dependence structures needed for transportability analyses."
  ["project_main_outcome_measure"]=>
  string(512) "The primary methodological outcome will be the difference between the transported treatment effect estimated using synthetic trial IPD and the corresponding transported treatment effect estimated using the original trial IPD.

Secondary performance measures will include:

absolute and relative bias;

confidence interval coverage;

agreement in effect direction;

agreement in statistical or clinical interpretation;

performance across different transportability estimators."
  ["project_main_predictor_indep"]=>
  string(756) "The main independent variable is the synthetic IPD reconstruction strategy used for each randomized trial. We will compare: (1) synthetic IPD reconstructed using only aggregate marginal information available from trial publications; and (2) synthetic IPD reconstructed using the same published information supplemented with external information on the joint distribution and dependence structure of relevant baseline covariates.

The original YODA trial IPD will serve as the empirical benchmark rather than as an independent variable. The primary comparison will assess whether the choice of synthetic-IPD reconstruction strategy affects the accuracy of transported treatment-effect estimates relative to those obtained using the original trial IPD."
  ["project_other_variables_interest"]=>
  string(946) "Other variables will include baseline participant characteristics required to describe the trial populations and perform transportability analyses. These may include age, sex, race/ethnicity, body mass index, smoking status, blood pressure, diabetes duration, HbA1c, renal function, cardiovascular history, relevant comorbidities, and baseline medication use, depending on availability and clinical relevance within each trial.

Variables will be harmonized across the YODA trials and external target-population data sources. Continuous variables will generally be retained as continuous measures and categorical variables will follow clinically meaningful or publication-reported categories. Particular attention will be given to variables considered potential effect modifiers, because preservation of their distributions and dependence structures is central to evaluating the performance of synthetic-IPD-based transportability analyses."
  ["project_stat_analysis_plan"]=>
  string(3625) "For each trial, synthetic IPD will first be reconstructed under the assumption that the original trial IPD are unavailable, using only information obtainable from trial publications and, where applicable, external data sources. The resulting synthetic datasets will then be used to estimate treatment effects in the corresponding external target populations using prespecified generalizability/transportability methods.

Separately, the same generalizability/transportability analyses will be performed using the original YODA IPD. The target-population treatment effect estimated from the original YODA IPD will serve as the primary empirical benchmark against which the synthetic-IPD-based transported treatment effects will be evaluated [2,7].

Strategy 1: Marginal-information reconstruction.

Synthetic trial participants will be generated using aggregate information typically available from trial publications, including sample size, treatment allocation, baseline covariate summaries, treatment-group outcomes, and available subgroup-specific treatment effects. Continuous and categorical covariates will be simulated to reproduce the reported marginal distributions. The generated data will then be iteratively calibrated so that prespecified published constraints are reproduced within an acceptable tolerance. This differs from conventional synthetic-data generation based on a generative model fitted to the original IPD; here, individual-level records are reconstructed from aggregate published constraints [12,13].

Strategy 2: Joint-structure-informed reconstruction.

In addition to the same published marginal information used in Strategy 1, external individual-level data will be used to estimate correlations or other dependence structures among relevant baseline covariates. These external dependence structures will be incorporated through multivariate simulation, such as copula-based or related approaches, while retaining the trial-specific marginal distributions [12,14]. This strategy will assess whether supplementing published marginal information with plausible joint covariate structure improves the accuracy of transported treatment-effect estimates.

The synthetic datasets will then be analyzed using the same generalizability/transportability estimators applied to the original YODA IPD, including outcome-model standardization, inverse probability weighting, calibration/weighting approaches, and doubly robust estimators [2,7].

As an intermediate validation step, we will assess how well each synthetic dataset reproduces characteristics of the original trial, including baseline covariate distributions, selected covariate dependence structures, overall treatment effects, and available subgroup treatment effects. These measures will be treated as process indicators of reconstruction fidelity rather than the primary validation outcome.

The primary evaluation will compare, for each trial and estimator, the target-population treatment effect obtained from each synthetic-IPD strategy with the corresponding target-population treatment effect obtained from the original YODA IPD. Performance will be summarized using absolute and relative differences in transported treatment effects, agreement in effect direction, and, where applicable, confidence interval coverage. The two synthetic-IPD strategies will be compared within and across trials. Repeated synthetic-data generation and sensitivity analyses will be used to assess robustness to assumptions regarding covariate distributions, dependence structures, and model specification."
  ["project_software_used"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(484) "Anticipated project start: October 2026

Data preparation and harmonization: October–December 2026

Synthetic IPD reconstruction and transportability analyses: January–April 2027

Comparison with original IPD benchmarks and sensitivity analyses: May–June 2027

Analysis completion: June 2027

Manuscript drafting: July–August 2027

First manuscript submission: September 2027

Results reported back to the YODA Project: September 2027"
  ["project_dissemination_plan"]=>
  string(804) "We anticipate producing at least one peer-reviewed methodological manuscript describing the development and empirical evaluation of synthetic-IPD-based transportability analyses. The primary target audience will include clinical epidemiologists, trialists, statisticians, and researchers interested in causal inference, generalizability, and transportability of randomized trial evidence.

Potential journals include Journal of Clinical Epidemiology, Clinical Trials, Epidemiology, Statistics in Medicine, or other relevant methodological and clinical research journals. Findings may also be presented at international epidemiology or clinical research conferences. Results and any required study reports will be submitted back to the YODA Project in accordance with the Data Use Agreement.

"
  ["project_bibliography"]=>
  string(3100) "

Dahabreh IJ, Robertson SE, Tchetgen EJ, Stuart EA and Hernán MA. Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals. Biometrics. 2019;75:685-694.
Dahabreh IJ, Robertson SE, Steingrimsson JA, Stuart EA and Hernán MA. Extending inferences from a randomized trial to a new target population. Statistics in Medicine. 2020;39:1999-2014.
Dahabreh IJ, Haneuse SJA, Robins JM, Robertson SE, Buchanan AL, Stuart EA and Hernán MA. Study Designs for Extending Causal Inferences From a Randomized Trial to a Target Population. Am J Epidemiol. 2021;190:1632-1642.
Dahabreh IJ and Hernán MA. Extending inferences from a randomized trial to a target population. Eur J Epidemiol. 2019;34:719-722.
Degtiar I and Rose S. A Review of Generalizability and Transportability. Annual Review of Statistics and Its Application. 2023;10:501-524.
Ackerman B, Lesko CR, Siddique J, Susukida R and Stuart EA. Generalizing randomized trial findings to a target population using complex survey population data. Statistics in Medicine. 2021;40:1101-1120.
Buchanan AL, Hudgens MG, Cole SR, Mollan KR, Sax PE, Daar ES, Adimora AA, Eron JJ and Mugavero MJ. Generalizing Evidence from Randomized Trials using Inverse Probability of Sampling Weights. J R Stat Soc Ser A Stat Soc. 2018;181:1193-1209.
Webster-Clark M and Keil AP. How Choice of Effect Measure Influences Minimally Sufficient Adjustment Sets for External Validity. American Journal of Epidemiology. 2023;192:1148-1154.
Manke-Reimers F, Brugger V, Bärnighausen T and Kohler S. When, why and how are estimated effects transported between populations? A scoping review of studies applying transportability methods. Eur J Epidemiol. 2025;40:255-273.
Chandross KJ, Meng Z, Ling AY, Montez-Rath ME, Carita P, Desai M, Lucats L, Kapphahn K and Sebastien B. An Overview of Current Methods for Real-world Applications to Generalize or Transport Clinical Trial Findings to Target Populations of Interest. Epidemiology. 2023;34:627-636.
Young JC, Dahabreh IJ, James S, Erlinge D, Fröbert O, Berglund A, Rylance R, Hernán MA and Matthews AA. Extending inferences from a randomized trial to trial-eligible and treatment-candidate target populations: examples of generalizability and transportability. Epidemiology (Cambridge, Mass). 2026.
 Bonofiglio F, Schumacher M and Binder H. Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: Applications to distributed computing under disclosure constraints. Statistics in Medicine. 2020;39:1183-1198.
Rott KW, Clark JM, Murad MH, Hodges JS and Huling JD. Causally interpretable meta-analysis combining aggregate and individual participant data. American Journal of Epidemiology. 2025;194:2060-2068.
Wang WJ, Bansal A, Bennette CS and Basu A. Mimicking Clinical Trials Using Real-World Data: A Novel Method and Applications. Medical Decision Making. 2023;43:275-287.

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  string(3) "n-a"
  [3]=>
  string(7) "erleada"
  [4]=>
  string(8) "tracleer"
  [5]=>
  string(8) "invokana"
  [6]=>
  string(8) "darzalex"
  [7]=>
  string(8) "prezista"
  [8]=>
  string(7) "dacogen"
  [9]=>
  string(7) "procrit"
  [10]=>
  string(8) "balversa"
  [11]=>
  string(8) "spravato"
  [12]=>
  string(8) "razadyne"
  [13]=>
  string(7) "simponi"
  [14]=>
  string(7) "tremfya"
  [15]=>
  string(9) "imbruvica"
  [16]=>
  string(8) "remicade"
  [17]=>
  string(8) "levaquin"
  [18]=>
  string(7) "opsumit"
  [19]=>
  string(8) "concerta"
  [20]=>
  string(33) "mouth-rinse-potassium-oxalate-1-4"
  [21]=>
  string(6) "invega"
  [22]=>
  string(15) "invega-sustenna"
  [23]=>
  string(7) "edurant"
  [24]=>
  string(16) "risperdal-consta"
  [25]=>
  string(9) "risperdal"
  [26]=>
  string(7) "uptravi"
  [27]=>
  string(9) "plivensia"
  [28]=>
  string(7) "topamax"
  [29]=>
  string(8) "yondelis"
  [30]=>
  string(7) "stelara"
}


num of trials
array(1) {
  [0]=>
  string(3) "240"
}


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