title: Evaluation of estimation, prediction and inference for autocorrelated latent variable modeling of binary data-a simulation study.
abstract: Longitudinal models of binary or ordered categorical data are often evaluated for adequacy by the ability of these to characterize the transition frequency and type between response states. Drug development decisions are often concerned with accurate prediction and inference of the probability of response by time and dose. A question arises on whether the transition probabilities need to be characterized adequately to ensure accurate response prediction probabilities unconditional on the previous response state. To address this, a simulation study was conducted to assess bias in estimation, prediction and inferences of autocorrelated latent variable models (ALVMs) when the transition probabilities are misspecified due to ill-posed random effects structures, inadequate likelihood approximation or omission of the autocorrelation component. The results may be surprising in that these suggest that characterizing autocorrelation in ALVMs is not as important as specifying a suitably rich random effects structure.
authors.affiliation: Ann Arbor Pharmacometrics Group (A2PG), 900 Victors Way, Suite 328, Ann Arbor, MI, 48108, USA. matt.hutmacher@a2pg.com.
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Hutmacher
mesh_terms: D003198:Computer Simulation; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D010600:Pharmacology / Q000706:statistics & numerical data*
keywords: Autocorrelation; Generalized nonlinear mixed-effects models; Latent variable; Probit