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Prediction-powered inference for clinical trials: application to linear covariate adjustment
Pierre-Emmanuel Poulet1, Maylis Tran1, Sophie Tezenas du Montcel1
1ARAMIS, Sorbonne Université, Paris, France.
Prediction-powered inference (PPI) enhances clinical trials by creating digital twins for patients, reducing sample size and control group needs. This statistically valid method improves treatment effect estimation in randomized clinical trials.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Machine Learning in Healthcare
Background:
- Standard statistical estimation methods in clinical trials can be inefficient.
- Leveraging machine learning for data augmentation is an emerging area.
- Prediction-powered inference (PPI) offers a novel approach to enhance statistical estimation.
Purpose of the Study:
- To apply the prediction-powered inference (PPI) paradigm in clinical trials.
- To utilize disease progression models for prognostic scores in participants.
- To provide untreated digital twins for treated patients in a statistically valid manner.
Main Methods:
- Employed prediction-powered inference (PPI) and its extension PPI++.
- Integrated disease progression models to generate prognostic scores based on baseline covariates.
- Conducted simulations to demonstrate theoretical properties and compared with regression-based covariate adjustment.
Main Results:
- The proposed estimator is asymptotically unbiased for the Average Treatment Effect.
- Derived an explicit formula for the variance of the estimator.
- Demonstrated potential sample size reduction in an Alzheimer's disease clinical trial setting.
Conclusions:
- Prediction-powered inference (PPI) can significantly reduce sample size requirements in clinical trials.
- The method supports imbalanced control-to-treated patient ratios, optimizing resource allocation.
- Enables the direct application of large-cohort disease prediction models to clinical trials.
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