Additive-Multiplicative Rates Model for Recurrent Event Data with Intermittently Observed Time-Dependent Covariates

Journal of Data Science

article

Tianmeng Lyu¹  Xianghua Luo²  Yifei Sun⁴ 

[1] Novartis Pharmaceuticals Corporation;Division of Biostatistics, School of Public Health, University of Minnesota;Biostatistics Core, Masonic Cancer Center, University of Minnesota;Department of Biostatistics, Mailman School of Public Health, Columbia University

关键词: additive-multiplicative rates model;    kernel smoothing;    recurrent events;    semiparametric method;    time-dependent covariates;   

DOI  :  10.6339/21-JDS1027

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Regression methods, including the proportional rates model and additive rates model, have been proposed to evaluate the effect of covariates on the risk of recurrent events. These two models have different assumptions on the form of the covariate effects. A more flexible model, the additive-multiplicative rates model, is considered to allow the covariates to have both additive and multiplicative effects on the marginal rate of recurrent event process. However, its use is limited to the cases where the time-dependent covariates are monitored continuously throughout the follow-up time. In practice, time-dependent covariates are often only measured intermittently, which renders the current estimation method for the additive-multiplicative rates model inapplicable. In this paper, we propose a semiparametric estimator for the regression coefficients of the additive-multiplicative rates model to allow intermittently observed time-dependent covariates. We present the simulation results for the comparison between the proposed method and the simple methods, including last covariate carried forward and linear interpolation, and apply the proposed method to an epidemiologic study aiming to evaluate the effect of time-varying streptococcal infections on the risk of pharyngitis among school children. The R package implementing the proposed method is available atwww.github.com/TianmengL/rectime .

【 授权许可】

CC BY