Hu C, Xu Z, Am Mendelsohn, Zhou H (2013) Latent variable indirect response modeling of categorical endpoints representing change from baseline.
Yano Y, Beal SL, Sheiner LB (2001) Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check. J Pharmacokinet Pharmacodyns 38:833–859ĭrezner Z, Wesolowsky GO (1990) On the computation of the bivariate normal integral. Hutmacher MM, French JL (2011) Extending the latent variable model for extra correlated longitudinal dichotomous responses. Lacroix BD, Lovern MR, Stockis A, Sargentini-Maier ML, Karlsson MO, Friberg LE (2009) A pharmacodynamic Markov mixed-effects model for determining the effect of exposure to certolizumab pegol on the ACR20. 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. 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.
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. Drug development decisions are often concerned with accurate prediction and inference of the probability of response by time and dose. 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.