title: A robust approach to t linear mixed models applied to multiple sclerosis data.
abstract: 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 auto-correlated, 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.
authors.affiliation: Department of Applied Mathematics, National Chung Hsing University, Taichung 402, Taiwan. tilin@amath.nchu.edu.tw
authors.forename: Tsung I
authors.initials: TI
authors.lastname: Lin
authors.forename: Jack C
authors.initials: JC
authors.lastname: Lee
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D002986:Clinical Trials as Topic; D004812:Epidemiologic Methods; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D009103:Multiple Sclerosis / Q000628:therapy; D016008:Statistical Distributions; D013269:Stochastic Processes