Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two‐stage stochastic dynamic treatment regime approach

Yasuhiro Hagiwara, Tomohiro Shinozaki, Hirofumi Mukai and Yutaka Matsuyama

Biometrics, 2021, vol. 77, issue 2, 702-714

Abstract: Subsequent treatments can result in a difficulty in interpretation of the overall survival results in confirmatory oncology clinical trials. To complement the intention‐to‐treat (ITT) analysis affected by subsequent treatment patterns unintentional in the trial protocol, several causal methods targeting the per‐protocol effect have been proposed. When two or more types of subsequent treatments are allowed in the trial protocol, however, these methods cannot answer clinical questions such as how sensitive the ITT analysis result is to higher or lower proportions of each subsequent treatment allowed in the trial protocol than observed, and to what extent ITT analysis result is generalizable to subsequent treatment patterns other than observed one. To answer these clinical questions, we propose a sensitivity analysis method for subsequent treatments using the inverse probability of treatment weighting method for stochastic dynamic treatment regimes (DTRs). We formulate oncology clinical trials with subsequent treatments as two‐stage designs in which initial treatments are randomized, but subsequent treatments are observational. In this formulation, we use stochastic DTRs to simulate specific proportions of each subsequent treatment and compare an initial experimental treatment with an initial control treatment under various proportions of each subsequent treatment. We applied our proposed method to a motivating randomized noninferiority trial for metastatic breast cancer. Simulation results are also reported to show the usefulness of the proposed method.

Date: 2021
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