Analysis of cyclic recurrent event data with multiple event types
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Recent Statistical Methods for Survival Analysis
Analysis of cyclic recurrent event data with multiple event types Chien‑Lin Su1 · Feng‑Chang Lin2 Received: 13 February 2020 / Accepted: 5 August 2020 © Japanese Federation of Statistical Science Associations 2020
Abstract Recurrent event data frequently arise in practice, and in some cases, the event process has cyclic or periodic components. We propose a semiparametric rate model with multiple event types that have such features. Generalized estimating equations are used for the estimation of regression coefficients after profiling the baseline rate function with a fully nonparametric estimator. The proposed estimators are shown to be consistent and asymptotically Gaussian. Their finite-sample behavior is assessed through simulation experiments. The predictability of the model with and without the cyclic component is also compared. With the cyclic component, our model improves the predictability of a conventional model without the cyclic feature. Data on recurrent fire alarms in Blenheim, New Zealand, are used for illustration purposes. Keywords Cyclic baseline rate function · Fire service · Generalized estimating equations · Marginal rate model · Prediction
1 Introduction Recurrent events are commonly observed in a long follow-up period in clinical studies. For example, young children often experience multiple asthma attacks (Duchateau et al. 2007), and cystic fibrosis patients tend to have recurrent pulmonary exacerbations of respiratory symptoms (Therneau and Grambsch 2013). In these studies,
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s4208 1-020-00088-7) contains supplementary material, which is available to authorized users. * Chien‑Lin Su chien‑[email protected] 1
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
2
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
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Japanese Journal of Statistics and Data Science
it is often of interest to investigate whether treatments or covariates are associated with the risk of clinical events. In the last 2 decades, statistical methods for the analysis of recurrent events have received much attention. Conventionally, recurrent events are modeled as the realization of an underlying counting process, where the calendar time or the time since enrollment is used as the time scale. Methods for evaluating the covariate effects in the regression analysis then fall into two categories: conditional methods and marginal methods. The conditional methods usually deal with intensity function that is conditional on the event history ( Prentice et al. 1981; Andersen and Gill 1982; Wang et al. 2001; Liu et al. 2004), while the marginal methods usually deal with rate function that is conditional only on the current values of covariates (Pepe and Cai 1993; Lawless and Nadeau 1995; Lin et al. 2000; Schaubel et al. 2006; Liu et al. 2010; Su et al. 2020). I
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