Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study

Qingxia Chen (), Fan Zhang, Ming-Hui Chen and Xiuyu Julie Cong
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Qingxia Chen: Vanderbilt University Medical Center
Fan Zhang: Pfizer Inc.
Ming-Hui Chen: University of Connecticut
Xiuyu Julie Cong: Everest Medicines

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 4, No 3, 685-707

Abstract: Abstract 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. In this paper, 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.

Keywords: Expectation–maximization algorithm; Model diagnostics; Semi-competing risk; Survival model; Time-varying treatment switching (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-020-09495-0

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