A robust approach to t linear mixed models applied to multiple sclerosis data

• Published in: Statistics in medicine Vol. 25; no. 8; pp. 1397 - 1412
• Main Authors: Lin, Tsung I., Lee, Jack C.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.04.2006; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Biometry - methods; Clinical trials; Clinical Trials as Topic; Epidemiologic Methods; Fisher scoring; Humans; Likelihood Functions; Linear Models; longitudinal data; Longitudinal Studies; Multiple sclerosis; Multiple Sclerosis - therapy; prediction; random effects; Regression analysis; Statistical Distributions; Stochastic Processes; t-REML
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.2384

We discuss a robust extension of linear mixed models based on the multivariate t distribution. Since longitudinal data are successively collected over time and typically tend to be autocorrelated, we employ a parsimonious first‐order autoregressive dependence structure for the within‐subject errors. A score test statistic for testing the existence of autocorrelation among the within‐subject errors is derived. Moreover, we develop an explicit scoring procedure for the maximum likelihood estimation with standard errors as a by‐product. The technique for predicting future responses of a subject given past measurements is also investigated. Results are illustrated with real data from a multiple sclerosis clinical trial. Copyright © 2005 John Wiley & Sons, Ltd.