A Multivariate Frailty Model for the Multi-Type Recurrent Event Data Using an Automated Monte Carlo EM Algorithm

• Activity Number: 244
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Khaled Bedair*+ and Yili Hong
• Companies: Virginia Tech and Virginia Polytechnic Institute & State University
• Keywords: MCEM algorithm ; Multiple type recurrent events ; Multivariate frailty Models ; Proportional hazard ; Random effects ; Survival analysis

Abstract

There has been an increasing interest in analyzing the multi-type recurrent event data. This type of data arises in many situations when two or more different event types may occur repeatedly over an observation period. For example, subjects are at risk of different disease types, causes of production stoppages, financial transactions in commerce, and insurance claims filed by holders. The interest in this setting is to characterize the incidence rate of event types, estimate the impact of covariates, and understand the dependence structure among event types. We propose a multivariate frailty proportional hazard model with multivariate distributions for the random effects to model the data. Maximum likelihood estimates of the regression coefficients, variance-covariance components, and the baseline hazard function are obtained based on a Monte Carlo EM algorithm. The E-step of the algorithm involves the calculation of the conditional expectations of the random effects by using the Metropolis-Hastings sampling. Louis' formula is applied to obtain the variance of the estimator. Simulation studies are presented to illustrate the performance of the proposed method.