Bayesian pattern-mixture models for dropout and intermittently missing data in longitudinal data analysis.

ABSTRACT: Valid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as missingness, is independent of the missing data. Data that are missing completely at random or missing at random are two data types for which missingness is ignorable. Given ignorable missingness, statistical inference can proceed without addressing the source of the missing data in the model. If the missingness is not ignorable, however, recommendations are to fit multiple models that represent different plausible explanations of the missing data. A popular choice in methods for evaluating nonignorable missingness is a random-effects pattern-mixture model that extends a random-effects model to include one or more between-subjects variables tha

SUBMITTER: Blozis SA 

PROVIDER: S-EPMC10990982 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature