Semiparametric recurrent event vs time-to-first-event analyses in randomized trials: Estimands and model misspecification

This is the peer reviewed version of the following article: “Zhong Y and Cook RJ (2021), Semiparametric recurrent event vs time-to-first-event analyses in randomized trials: Estimands and model misspecification, Statistics in Medicine, 40 (16): 3823–3842” which has been published in final form at https://doi.org/10.1002/sim.9002.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 undermodel 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.National Natural Science Foundation of China, NSFC-11901376 (to YZ) || Shanghai Pujiang Program, 2019PJC051 (to YZ) || SUFE Innovation Funding, 2019110051 (to YZ) || Discovery Grant and Supplement Award from the Natural Science and Engineering Research Council of Canada, RGPIN 155849 and RGPIN 04207 (to RJC) || Canadian Institutes for Health Research, FRN 13887 (to RJC