Mixed Model Analysis of Censored Longitudinal Data with Flexible Random Effects Density

• Activity Number: 34
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): David Michael Vock*+ and Marie Davidian and Anastasios Tsiatis and Andrew J. Muir
• Companies: North Carolina State University and North Carolina State University and North Carolina State University and Duke Clinical Research Institute
• Keywords: Longitudinal Data ; Random Effects ; Censoring ; Limit of Quantification

Abstract

Mixed effects models are commonly used to represent longitudinal data. An additional complication arises when the response is censored, which often occurs in pharmacokinetic studies due to limits of quantification of the assay used. While Gaussian random effects are routinely assumed, little work has characterized the consequences of misspecifying the random effects distribution for censored longitudinal data, nor has a more flexible distribution been studied in this scenario. We show that maximum likelihood estimators will not be consistent when the random effects density is misspecified, and the effect of misspecification is greatest when the true random effects density deviates substantially from normality and the number of non-censored observations on each subject is small. We develop a mixed model framework for censored longitudinal data in which the random effects are represented by the flexible seminonparameteric (SNP) density and show how to obtain estimates in SAS Proc NLMIXED. Simulations show that this approach can lead to reduction in bias relative to assuming Gaussian random effects, and the methods are demonstrated on data from an industry study of hepatitis C virus.