Du: Simultaneous fixed and random effects selection in finite mixtures of linear mixed-effects models | Harel: Measuring fatigue in systemic sclerosis: a comparison of the SF-36 vitality subscale and FACIT fatigue scale using item response theory

Date: 2012-02-03

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Du: Linear mixed-effects (LME) models are frequently used for modeling longitudinal data. One complicating factor in the analysis of such data is that samples are sometimes obtained from a population with significant underlying heterogeneity, which would be hard to capture by a single LME model. Such problems may be addressed by a finite mixture of linear mixed-effects (FMLME) models, which segments the population into subpopulations and models each subpopulation by a distinct LME model. Often in the initial stage of a study, a large number of predictors are introduced. However, their associations to the response variable vary from one component to another of the FMLME model. To enhance predictability and to obtain a parsimonious model, it is of great practical interest to identify the important effects, both fixed and random, in the model. Traditional variable selection techniques such as stepwise deletion and subset selection are computationally expensive as the number of covariates and components in the mixture model increases. In this talk, we introduce a penalized likelihood approach and propose a nested EM algorithm for efficient numerical computations. Our estimators are shown to possess desirable properties such as consistency, sparsity and asymptotic normality. We illustrate the performance of our method through simulations and a systemic sclerosis data example.

Harel: Multi-item, self-reported questionnaires are frequently used to measure aspects of health-related quality of life. Due to the latent nature of the constructs underlying these instruments, Item Response Theory models are often used to relate the observed item scores to the latent trait. However, there are no well-established guidelines for how to compare two such questionnaires. In this talk I will explore graphical methods for the comparison of multi-item self-reported questionnaires by using Partial Credit Models. This will be illustrated with the comparison of two fatigue questionnaires in patients with Systemic Sclerosis.

Speaker

Ye Ting Du is an M.Sc. student in our department. He works with Abbas Khalili and Johanna Neslehova.

Daphna Harel is a Ph.D. candidate in our department. She works with Russell Steele.