title: A Latent Transition Analysis Model for Latent-State-Dependent Nonignorable Missingness.
abstract: Psychologists often use latent transition analysis (LTA) to investigate state-to-state change in discrete latent constructs involving delinquent or risky behaviors. In this setting, latent-state-dependent nonignorable missingness is a potential concern. For some longitudinal models (e.g., growth models), a large literature has addressed extensions to accommodate nonignorable missingness. In contrast, little research has addressed how to extend the LTA to accommodate nonignorable missingness. Here we present a shared parameter LTA that can reduce bias due to latent-state-dependent nonignorable missingness: a parallel-process missing-not-at-random (MNAR-PP) LTA. The MNAR-PP LTA allows outcome process parameters to be interpreted as in the conventional LTA, which facilitates sensitivity analyses assessing changes in estimates between LTA and MNAR-PP LTA. In a sensitivity analysis for our empirical example, previous and current membership in high-delinquency states predicted adolescents' membership in missingness states that had high nonresponse probabilities for some or all items. A conventional LTA overestimated the proportion of adolescents ending up in a low-delinquency state, compared to an MNAR-PP LTA.
authors.affiliation: Quantitative Methods Program, Department of Psychology and Human Development, Vanderbilt University, Peabody #552, 230 Appleton Place, Nashville, TN, 37203 , USA. sonya.sterba@vanderbilt.edu.
authors.forename: Sonya K
authors.initials: SK
authors.lastname: Sterba
mesh_terms: D000293:Adolescent; D006801:Humans; D007604:Juvenile Delinquency; D015233:Models, Statistical; D011336:Probability; D011594:Psychometrics; D012309:Risk-Taking; D013223:Statistics as Topic; D055815:Young Adult
keywords: latent transition analysis; missing not at random; mixture model; nonignorable missing data; shared parameter model