Additive rates model for recurrent event data with intermittently observed time-dependent covariates

Tianmeng Lyu, Xianghua Luo, Chiung Yu Huang, Yifei Sun

Research output: Contribution to journalArticlepeer-review

Abstract

Various regression methods have been proposed for analyzing recurrent event data. Among them, the semiparametric additive rates model is particularly appealing because the regression coefficients quantify the absolute difference in the occurrence rate of the recurrent events between different groups. Estimation of the additive rates model requires the values of time-dependent covariates being observed throughout the entire follow-up period. In practice, however, the time-dependent covariates are usually only measured at intermittent follow-up visits. In this paper, we propose to kernel smooth functions involving time-dependent covariates across subjects in the estimating function, as opposed to imputing individual covariate trajectories. Simulation studies show that the proposed method outperforms simple imputation methods. The proposed method is illustrated with data from an epidemiologic study of the effect of streptococcal infections on recurrent pharyngitis episodes.

Original languageEnglish (US)
Pages (from-to)2239-2255
Number of pages17
JournalStatistical methods in medical research
Volume30
Issue number10
Early online dateAug 26 2021
DOIs
StatePublished - Oct 2021

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Lyu and Luo were partially supported by the U.S. National Institutes of Health (R03MH112895). Luo was also supported by the U.S. National Institutes of Health (P30CA077598). Huang was supported by the U.S. National Institutes of Health (R01CA193888).

Publisher Copyright:
© The Author(s) 2021.

Keywords

  • Kernel smoothing
  • additive rates models
  • estimating equations
  • recurrent events
  • time-dependent covariates

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