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Semiparametric recurrent event vs time‐to‐first‐event analyses in randomized trials: Estimands and model misspecification
Author(s) -
Zhong Yujie,
Cook Richard J.
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9002
Subject(s) - censoring (clinical trials) , estimator , econometrics , proportional hazards model , event (particle physics) , semiparametric regression , semiparametric model , sample size determination , statistics , computer science , mathematics , physics , quantum mechanics
Insights regarding the merits of recurrent event and time‐to‐first‐event analyses are needed to provide guidance on strategies for analyzing intervention effects in randomized trials involving recurrent event responses. Using established asymptotic results we introduce a framework for studying the large sample properties of estimators arising from semiparametric proportional rate function models and Cox regression under model misspecification. The asymptotic biases and power implications are investigated for different data generating models, and we study the impact of dependent censoring on these findings. Illustrative applications are given involving data from a cystic fibrosis trial and a carcinogenicity experiment, following which we summarize findings and discuss implications for clinical trial design.