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Activity Number: 18 - Survival Analysis Developments for Improving Medical Decision Making
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #327234
Title: Time-Dependent Covariates in Recurrent Event Models
Author(s): Xianghua Luo* and Tianmeng Lyu and Yifei Sun and Chiung-Yu Huang
Companies: University of Minnesota, School of Public Health and University of Minnesota and Columbia University and University of California at San Francisco
Keywords: Imputation for missing data; Kernel smoothing; Recurrent events; Time-dependent covariates

In follow-up studies, investigators are often interested in evaluating the effects of time-dependent covariates on recurrent event risks. Theoretically, the usual proportional or additive rates model (PRM/ARM) allows time-dependent covariates, yet the estimation requires such covariates to be observed for all at-risk individuals at all event times (in PRM) or more unrealistically, throughout the follow-up (in ARM). In practice, simple methods such as the last value carried forward method have been used to approximate individual covariate trajectories until recently kernel-smoothed estimating functions were proposed for the PRM. Currently, our team is extending the ARM using similar techniques. In this talk, we focus on some frequently overlooked design/analysis issues shared by these models: 1) both time-dependent and -independent covariates being in the model; 2) non-synchronized measurements of different time-dependent covariates; 3) whether to use baseline measurements of time-dependent covariates in estimation; 4) time-dependent covariates not being measured at event times. We hope that the insights we share here can help inform the design of longitudinal studies in the future.

Authors who are presenting talks have a * after their name.

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