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

TitleEstimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study.
Publication TypeJournal Article
Year of Publication2020
AuthorsChen, Qingxia, Fan Zhang, Ming-Hui Chen, and Xiuyu Julie Cong
JournalLifetime Data Anal
Volume26
Issue4
Pagination685-707
Date Published2020 10
ISSN1572-9249
KeywordsAlgorithms, Computer Simulation, Head and Neck Neoplasms, Humans, Likelihood Functions, Randomized Controlled Trials as Topic, Survival Analysis, Treatment Switching
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.

DOI10.1007/s10985-020-09495-0
Alternate JournalLifetime Data Anal
Original PublicationEstimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study.
PubMed ID32125594
PubMed Central IDPMC7483904
Grant ListR01 CA237895 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
P01CA142538 / NH / NIH HHS / United States
R21HL097334 / NH / NIH HHS / United States
R21 HL097334 / HL / NHLBI NIH HHS / United States
1UL1RR02497 / NH / NIH HHS / United States
GM70335 / NH / NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States