Open AccessEstimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study
Author(s) -
Qingxia Chen,
Fan Zhang,
MingHui Chen,
Xiuyu Julie Cong
Publication year - 2020
Publication title -
lifetime data analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.677
H-Index - 46
eISSN - 1572-9249
pISSN - 1380-7870
DOI - 10.1007/s10985-020-09495-0
Subject(s) - computer science , head and neck squamous cell carcinoma , maximum likelihood , expectation–maximization algorithm , class (philosophy) , software , head and neck , clinical trial , maximization , treatment effect , econometrics , mathematical optimization , head and neck cancer , algorithm , statistics , artificial intelligence , mathematics , medicine , radiation therapy , surgery , traditional medicine , programming language
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.