Document Type
Pre-Print
Publication Date
1-18-2025
Abstract
Prediction-powered inference (PPI) [1] and its subsequent development called PPI++ [2] provide a novel approach to standard statistical estimation leveraging machine learning systems to enhance unlabeled data with predictions. We use this paradigm in clinical trials. The predictions are provided by disease progression models, providing prognostic scores for all the participants as a function of baseline covariates. The proposed method would empower clinical trials by providing untreated digital twins of the treated patients while remaining statistically valid. The potential implications of this new estimator of the treatment effect in a two-arm randomized clinical trial (RCT) are manifold. First, it leads to an overall reduction of the sample size required to reach the same power as a standard RCT. Secondly, it advocates for an imbalance of controls and treated patients, requiring fewer controls to achieve the same power. Finally, this technique directly transfers any disease prediction model trained on large cohorts to practical and scientifically valid use. In this paper, we demonstrate the theoretical properties of this estimator and illustrate them through simulations. We show that it is asymptotically unbiased for the Average Treatment Effect and derive an explicit formula for its variance. An application to an Alzheimer's disease clinical trial showcases the potential to reduce the sample size.
Rights
Copyright 2025 The Authors.
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DOI
10.1101/2025.01.15.25320578
Persistent Identifier
https://archives.pdx.edu/ds/psu/43230
Publisher
Cold Spring Harbor Laboratory
Citation Details
Poulet, P.-E., Tran, M., Tezenas du Montcel, S., Dubois, B., Durrleman, S., & Jedynak, B. (2025). Prediction-powered Inference for Clinical Trials.
Description
This is an author manuscript made available under a CC-BY 4.0 International license.