A Multiple Imputation Approach to the Analysis of Clustered Interval-Censored Failure Time Data with the Additive Hazards Model

• Activity Number: 457
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Ling Chen*+ and Tony Sun
• Companies: Washington University in St. Louis and University of Missouri
• Keywords: additive hazards model ; clustered data ; interval-censoring ; multiple imputation ; regression analysis

Abstract

This paper discusses regression analysis of clustered interval-censored failure time data, which means the failure times of interest are correlated within small groups instead of being independent. This type of data occurs in many fields such as medical studies. For the problem, we focus on the situation where the survival time of interest can be described by the additive hazards model and a multiple imputation approach is presented for inference. A major advantage of the approach is its simplicity and it can be easily implemented by using the existing methods for right-censored failure time data. Extensive simulation studies are conducted which indicate that the approach performs well for practical situations and is comparable to the existing methods. The methodology is applied to a set of clustered interval-censored failure time data from a motivating example.