Incorporating external data for analyzing randomized clinical trials: A transfer learning approach

TL;DR

This paper introduces a transfer learning framework for clinical trial analysis that leverages external data to improve estimation accuracy and reduce sample size requirements, supported by theoretical guarantees and numerical validation.

Contribution

It develops a formal transfer learning approach for clinical trials, including a new estimator, theoretical analysis, and inference methods, addressing data bias and sample size issues.

Findings

Transfer learning reduces sample size needed for accurate treatment effect estimation.

The proposed estimator is robust and effective across different scenarios.

Theoretical guarantees include convergence rates and asymptotic normality.

Abstract

Randomized clinical trials are the gold standard for analyzing treatment effects, but high costs and ethical concerns can limit recruitment, potentially leading to invalid inferences. Incorporating external trial data with similar characteristics into the analysis using transfer learning appears promising for addressing these issues. In this paper, we present a formal framework for applying transfer learning to the analysis of clinical trials, considering three key perspectives: transfer algorithm, theoretical foundation, and inference method. For the algorithm, we adopt a parameter-based transfer learning approach to enhance the lasso-adjusted stratum-specific estimator developed for estimating treatment effects. A key component in constructing the transfer learning estimator is deriving the regression coefficient estimates within each stratum, accounting for the bias between source and target data. To provide a theoretical foundation, we derive the $l_1$ convergence rate for the estimated regression coefficients and establish the asymptotic normality of the transfer learning estimator. Our results show that when external trial data resembles current trial data, the sample size requirements can be reduced compared to using only the current trial data. Finally, we propose a consistent nonparametric variance estimator to facilitate inference. Numerical studies demonstrate the effectiveness and robustness of our proposed estimator across various scenarios.