Regression analysis of multivariate current status data with dependent censoring: application to ankylosing spondylitis data

Abstract

Multivariate current-status failure time data consist of several possibly related event times of interest, in which the status of each event is determined at a single examination time. If the examination time is intrinsically related to the event times, the examination is referred to as dependent censoring and needs to be taken into account. Such data often occur in clinical studies and animal carcinogenicity experiments. To accommodate for possible dependent censoring, this paper proposes a joint frailty model for event times and dependent censoring time. We develop a likelihood approach using Gaussian quadrature techniques for obtaining maximum likelihood estimates. We conduct extensive simulation studies for investigating finite-sample properties of the proposed method. We illustrate the proposed method with an analysis of patients with ankylosing spondylitis, where the examination time may be dependent on the event times of interest.

Semiparametric Regression Analysis of Interval-Censored Data with Informative Dropout

“…Zhang et al. (), Chen et al., , and Ma et al. () studied this problem for current status data (Huang, ) by assuming a frailty model or copula structure for the event time of interest and the examination time.…”

Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm

“…Callegaro and Lacobeli (2012) proposed a Cox model with log-skewed-normal frailties for clustered right-censored data. Chen et al (2014) studied multivariate current status data under univariate informative censoring. For a more in depth review of frailty modeling techniques in survival analysis, please see Hougaard (2000), Ibrahim et al (2008), and Wienke (2012).…”