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MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent.(More)
Random-effects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the random-effects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of random-effects pattern-mixture models to further handle and describe the influence of(More)
A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data. This model is developed for both the probit and logistic response functions. The threshold concept is used, in which it is assumed that the observed ordered category is determined by the value of a latent unobservable continuous response(More)
OBJECTIVE To determine the prevalence and correlates of psychiatric disorders among preschool children in a primary care pediatric sample. METHOD In a two-stage design, 3,860 preschool children were screened; 510 received fuller evaluations. RESULTS For quantitative assessment of disorder (> or = 90th percentile), prevalence of behavior problems was(More)
OBJECTIVES To determine any long-term effects, 6 and 8 years after childhood enrollment, of the randomly assigned 14-month treatments in the NIMH Collaborative Multisite Multimodal Treatment Study of Children With Attention-Deficit/Hyperactivity Disorder (MTA; N = 436); to test whether attention-deficit/hyperactivity disorder (ADHD) symptom trajectory(More)
We develop simultaneous approximate statistical prediction limits for a gamma-distributed random variable. Specifically, we develop an upper prediction limit (UPL) for p of m future samples at each of r locations, based on a previous sample of n measurements. A typical example is the environmental monitoring problem in which the distribution of an analyte(More)