THE ANALYSIS OF REPEATED-MEASURES DATA ON SCHIZOPHRENIC REACTION-TIMES USING MIXTURE-MODELS

Reaction times for schizophrenic individuals in a simple visual tracki ng experiment can be substantially more variable than for non-schizoph renic individuals. Current psychological theory suggests that at least some of this extra variability arises from an attentional lapse that delays some, but not all, of each schizophrenic's reaction times. Base d on this theory, we pursue models in which measurements from non-schi zophrenics arise from a normal linear model with a separate mean for e ach individual, whereas measurements from schizophrenics arise from a mixture of (i) a component analogous to the distribution of response t imes for non-schizophrenics and (ii) a mean-shifted component, We fit four mixture models within this framework, where the distinctions betw een models arise from assumptions about the variance of the shifted ob servations and the exchangeability of schizophrenic individuals. Some of these models can be fit by maximum likelihood using the EM algorith m, and all can be fit using the ECM algorithm, where the covariance ma trices associated with the parameters are calculated by the SEM and SE CM algorithms, respectively. Bayesian model monitoring using posterior predictive checks is invoked to discard models that fail to reproduce certain observed features of the data and to stimulate the developmen t of better models.