A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.

ABSTRACT: Studies of memory trajectories using longitudinal data often result in highly nonrepresentative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study, we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semiparametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate the sensitivity of the results to untestable as

SUBMITTER: Josefsson M 

PROVIDER: S-EPMC10102880 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature