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Activity Number: 233 - Statistical Considerations for Adjusting Overall Survival in Randomized Trials with Treatment Switching
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #322126
Title: Estimation of Treatment Effects and Model Diagnostics with Two-Way, Time-Varying Treatment Switching
Author(s): Qingxia Chen and Fan Zhang* and Ming-Hui Chen and Xiuyu Julie Cong
Companies: Vanderbilt University Medical Center and Pfizer Inc., Groton, CT. This work was done at University of Connecticut. and University of Connecticut and Everest Medicines, Shanghai, China. This work was done at Boehringer Ingelheim Pharma.
Keywords: Expectation-maximization algorithm; Model diagnostics; Semi-competing risk; Survival model; Time-varying treatment switching

Treatment switching frequently occurs in clinical trials due to ethical reasons. Intent-to-treat analysis without adjusting for switching yields biased and inefficient estimates of the treatment effects. We propose a class of semiparametric semi-competing risks transition survival models to accommodate two-way time-varying switching. Theoretical properties of the proposed method are examined. An efficient expectation-maximization algorithm is derived to obtain maximum likelihood estimates and model diagnostic tools. Existing software is used to implement the algorithm. Simulation studies are conducted to demonstrate the validity of the model. The proposed method is further applied to data from a clinical trial with patients having recurrent or metastatic squamous-cell carcinoma of head and neck.

Authors who are presenting talks have a * after their name.

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