B0391
Title: A Bayesian semi-parametric approach for inference on the PPCM from longitudinal data with dropout
Authors: Maria Josefsson - Umea School of Business, Economics and Statistics (Sweden) [presenting]
Michael Daniels - University of Florida (United States)
Sara Pudas - Umea University (Sweden)
Abstract: Studies of memory trajectories using longitudinal data often result in highly non-representative 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. 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 semi-parametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate the sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimating lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.
