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  string(1614) "Background: Borrowing external control data can improve clinical trial efficiency, but differences in patient populations and outcomes across studies risk introducing bias. No existing method provides theoretical guarantees under covariate-adaptive randomization or when nearly all patients receive treatment.

Objective: To validate the Source-Weighted Least Squares (SWLS) method, which automatically selects compatible external control sources and produces more precise treatment effect estimates while maintaining unbiasedness.

Study Design: Secondary analysis of existing Phase 3 randomized controlled trial data. One trial is designated as the internal trial; placebo/comparator arms from remaining trials serve as candidate external control sources.

Participants: Adults with type 2 diabetes enrolled in the CANTATA canagliflozin trial program (NCT01081834, NCT01106677, NCT01137812, NCT01106625, NCT01106690, NCT01106651, NCT01809327, NCT00968812, NCT01381900, NCT02025907).

Primary and Secondary Outcome Measures: The primary outcome is change in HbA1c from baseline. Secondary outcomes include fasting plasma glucose, body weight, and systolic blood pressure. These are used to estimate treatment effects and assess borrowing performance.

Statistical Analysis: SWLS regression adjusting for baseline covariates with penalized weight optimization to select compatible sources. Performance is evaluated by comparing bias, standard error, and 95% confidence interval coverage between SWLS and internal-only estimates across different internal/external trial configurations."
  ["project_brief_bg"]=>
  string(3071) "Randomized controlled trials are the gold standard for evaluating treatment efficacy. In rare diseases, enrolling sufficient control patients may be infeasible or ethically questionable. Regulatory agencies including the FDA have increasingly recognized the potential of external control data to supplement or replace concurrent controls (see, e.g., Jahanshahi et al., 2021; Purpura et al., 2022). However, naïve pooling of external data risks bias when patient populations differ in baseline characteristics (covariate heterogeneity, that is, the baseline covariates differ for the internal and external data sources) or in outcome distributions (outcome heterogeneity, that is, the  outcomes depends on covariates through different statistical models).

Existing approaches address these challenges only partially. Covariate adjustment methods such as matching, propensity score weighting, and calibration weighting handle population differences but ignore outcome-level incompatibility. Bayesian borrowing methods like power priors and meta-analytic predictive priors discount based on outcome conflict but treat covariate comparability as fixed. Recent integrative approaches (Gao et al., 2025; Gu et al., 2024) address both. Specifically, Gao et al. considers a calibration weighting approach for covariate heterogeneity and then add selection weights to discard external data that introduce biases; Gu et al. addresses covariate heterogeneity by weighting and outcome heterogeneity by debiasing the estimator. Only Gu et al.,2024 provides valid inference under covariate-adaptive randomization (CAR) and no method discusses extreme allocation regimes where nearly all patients receive active treatment.

This project validates Source-Weighted Least Squares (SWLS), a new statistical framework that fills these gaps. The requested CANTATA canagliflozin trial data provides an ideal real-data application of this study. It has multiple Phase 3 RCTs sharing a common drug, disease, and baseline covariates, with known between-trial differences in design and population, allowing rigorous assessment of the method's ability to detect compatibility and improve precision without bias.

References:

Gao, C., Yang, S., Shan, M., Ye, W., Lipkovich, I., & Faries, D. (2025). Improving randomized controlled trial analysis via data-adaptive borrowing. Biometrika, 112(2), asae069.

Gu, Y., Liu, H., & Ma, W. (2024). Incorporating external data for analyzing randomized clinical trials: A transfer learning approach. arXiv preprint arXiv:2409.04126.

Jahanshahi, M., Gregg, K., Davis, G., Ndu, A., Miller, V., Vockley, J., ... & Sakai, S. (2021). The use of external controls in FDA regulatory decision making. Therapeutic Innovation & Regulatory Science, 55(5), 1019-1035.

Purpura, C. A., Garry, E. M., Honig, N., Case, A., & Rassen, J. A. (2022). The role of real‐world evidence in FDA‐approved new drug and biologics license applications. Clinical Pharmacology & Therapeutics, 111(1), 135-144."
  ["project_specific_aims"]=>
  string(526) "Aim 1: Evaluate whether SWLS correctly identifies compatible versus incompatible external control sources across multiple configurations of the CANTATA trials.

Aim 2: Test whether SWLS produces narrower confidence intervals and smaller standard errors for the HbA1c treatment effect compared to internal-only analysis, without introducing bias.

Aim 3: Evaluate rare-disease regimes. Assess whether SWLS restores valid inference in settings where conventional estimators fail due to insufficient control-arm data."
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    ["label"]=>
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      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
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      ["label"]=>
      string(36) "Participant-level data meta-analysis"
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      string(37) "participant_level_data_only_from_yoda"
      ["label"]=>
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      ["label"]=>
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  string(913) "We will use all patients from the requested CANTATA canagliflozin trials who were randomized and have a baseline HbA1c measurement and at least one post-baseline HbA1c assessment. No additional demographic or clinical exclusion criteria will be applied beyond those already defined in each trial's protocol.

Specifically, for each analysis configuration, we will extract two groups from each trial: (1) the placebo or active-comparator arm, which serves as a candidate external control source, and (2) the canagliflozin treatment arm. When a trial is designated as the internal trial, both its treatment and control arms are used. When a trial serves as an external source, only its control/comparator arm is used. No data from studies outside the YODA Project will be used. All analyses will be conducted on a single secure platform using the individual patient-level SDTM datasets provided through YODA. "
  ["project_main_outcome_measure"]=>
  string(572) "Primary Outcome Measure:

Change in HbA1c (%) from baseline to the primary assessment timepoint defined in each trial's protocol (typically Week 26). This is the endpoint used to estimate the treatment effect of canagliflozin and to evaluate SWLS performance (bias, standard error, and confidence interval coverage) relative to the internal-only estimator.

Secondary Outcome Measures:

Change in fasting plasma glucose (mg/dL) from baseline.

Change in body weight (kg) from baseline.

Change in systolic blood pressure (mmHg) from baseline."
  ["project_main_predictor_indep"]=>
  string(123) "This is a main statistical and biostatistical research project. We will select the independent variables based on the data."
  ["project_other_variables_interest"]=>
  string(168) "We will select the important covariates based on the data from the 10 studies (the participant-level data). These covariates are then used in the designs and inference."
  ["project_stat_analysis_plan"]=>
  string(1666) "1.For each trial, we will summarize baseline characteristics (age, sex, HbA1c, body weight, fasting plasma glucose) by treatment arm using means, standard deviations, medians, and proportions as appropriate.

2. For each designated internal trial, we will fit a linear regression of the primary outcome (change in HbA1c) on treatment assignment and baseline covariates. The estimated treatment effect coefficient, its standard error, and 95% Wald confidence interval will serve as the benchmark against which SWLS is compared. This analysis will be repeated for each secondary outcome.

3. For each configuration—where one trial serves as the internal trial and the remaining trials' control arms serve as candidate external sources—we will apply the SWLS procedure to enhance the estimation of treatment effect.

4. Across all internal/external configurations, we will report:

Point estimates and confidence intervals for the treatment effect from both internal-only and SWLS analyses.

Standard error ratio (SWLS / internal-only) to quantify efficiency gain.

Source weights assigned to each external trial, indicating which sources were selected as compatible.

Consistency of results across configurations to assess stability.

5. To evaluate SWLS under extreme allocation regimes, we will subsample the internal trial's control arm to artificially create scenarios where the control-to-total ratio is small (e.g., 10%, 5%).  We will compare SWLS and internal-only estimates under these reduced control conditions, assessing whether SWLS restores reliable inference where the internal-only estimator degrades."
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  ["project_timeline"]=>
  string(923) "Project initial start date: 2025.8

After approval of data

Month 1: Upon data access approval. Initial activities include data download, quality checks, and harmonization of variables across CANTATA trial datasets. Finalize the analytic dataset with standardized baseline covariates and outcomes across all trials. Complete descriptive analyses and between-trial comparisons. 

Month 2: Complete all SWLS and internal-only analyses across trial configurations for the primary outcome (HbA1c), including source selection evaluation and efficiency gain quantification. Complete analyses for secondary outcomes, rare-disease subsampling experiments, and all sensitivity analyses.

Month 3: Complete draft incorporating theoretical background, real-data results, and discussion of practical implications for trial design.

Month 4: Submission to a peer-reviewed statistical or biomedical journal."
  ["project_dissemination_plan"]=>
  string(289) "We will publish two to three papers for (bio)statisticians and clinicians.

Here are some possible journals: Journal of Royal Statistical Society, Series B, Journal of American Statistical Association, Biometrika, Annals of Statistics, Statistica Sinica, Statistics in Medicine, etc."
  ["project_bibliography"]=>
  string(922) "
References:

Gao, C., Yang, S., Shan, M., Ye, W., Lipkovich, I., & Faries, D. (2025). Improving randomized controlled trial analysis via data-adaptive borrowing. Biometrika, 112(2), asae069.

Gu, Y., Liu, H., & Ma, W. (2024). Incorporating external data for analyzing randomized clinical trials: A transfer learning approach. arXiv preprint arXiv:2409.04126.

Jahanshahi, M., Gregg, K., Davis, G., Ndu, A., Miller, V., Vockley, J., … & Sakai, S. (2021). The use of external controls in FDA regulatory decision making. Therapeutic Innovation & Regulatory Science, 55(5), 1019-1035.

Purpura, C. A., Garry, E. M., Honig, N., Case, A., & Rassen, J. A. (2022). The role of real‐world evidence in FDA‐approved new drug and biologics license applications. Clinical Pharmacology & Therapeutics, 111(1), 135-144.
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