Multilevel modeling of cognitive function in schizophrenic patients and their first degree relatives

Abstract

We describe multilevel modeling of cognitive function in subjects with schizophrenia, their healthy first degree relatives and controls. The purpose of the study was to compare mean cognitive performance between the three groups after adjusting for various covariates, as well as to investigate differences in the variances. Multilevel models were required because subjects were nested within families and some of the measures were repeated several times on the same subject. The following four methodological issues that arose during the analysis of the data are discussed. First, when the random effects distribution was not normal, non-parametric maximum likelihood (NPML) was employed, leading to a different conclusion than the conventional multilevel model regarding one of the main study hypotheses. Second, the between-subject (within-family) variance was allowed to differ between the three groups. This corresponded to the variance at level 1 or level 2 depending on whether repeated measures were analyzed. Third, a positively skewed response was analyzed using a number of different generalized linear mixed models. Finally, penalized quasilikelihood (PQL) estimates for a binomial response were compared with estimates obtained using Gaussian quadrature. A small simulation study was carried out to assess the accuracy of the latter.