Semiparametric regression analysis for alternating recurrent event data

Abstract

Alternating recurrent event data arise frequently in clinical and epidemiologic studies, where two types of events such as hospital admission and discharge occur alternately over time. The two alternating states defined by these recurrent events could each carry important and distinct information about a patient’s underlying health condition and/or the quality of care. In this paper, we propose a semiparametric method for evaluating covariate effects on the two alternating states jointly. The proposed methodology accounts for the dependence among the alternating states as well as the heterogeneity across patients via a frailty with unspecified distribution. Moreover, the estimation procedure, which is based on smooth estimating equations, not only properly addresses challenges such as induced dependent censoring and intercept sampling bias commonly confronted in serial event gap time data, but also is more computationally tractable than the existing rank-based methods. The proposed methods are evaluated by simulation studies and illustrated by analyzing psychiatric contacts from the South Verona Psychiatric Case Register.

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

“…Recurrent events data along with terminal event and covariates are discussed by Andersen et al [35], Han et al [36], Han et al [37], Kim [38] and Sun et al [39]. Frailty are sometimes also included such as in the work by Deep et al [40], Lee et al [41], Lin et al [42] and Slimáček and Lindqvist [43]. In the four articles, an estimation method was provided.…”

BivRec: an R package for the nonparametric and semiparametric analysis of bivariate alternating recurrent events

“…1 . It provides a consistent and user-friendly set of functions to: explore and visualize the data, estimate and plot the joint cumulative distribution function ( cdf ), the marginal survival and the conditional cdf using nonparametric methods [ 1 ] and fit semiparametric accelerated failure time models [ 2 , 3 ] to estimate the effect of covariates on the two alternating gap times.…”

Penalized survival models for the analysis of alternating recurrent event data

“…Like Huang and Wang (), Lee et al . () does not provide information on the correlations within or between the two recurrent event sequences.…”

“…A nonparametric approach was developed by Huang and Wang (2005). Recently, Lee et al (2018) developed an estimating equation approach for accelerated failure time model for an alternating recurrent event data. In their proposed estimating procedure, the distribution of the possibly correlated random variables for two processes was left unspecified.…”

“…In their proposed estimating procedure, the distribution of the possibly correlated random variables for two processes was left unspecified. Like Huang and Wang (2005), Lee et al (2018) does not provide information on the correlations within or between the two recurrent event sequences.…”