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Studies of memory trajectories using longitudinal data often result in highlynon-representative samples due to selective study enrollment and attrition. Anadditional bias comes from practice effects that result in improved ormaintained performance due to familiarity with test content or context. Thesechallenges may bias study findings and severely distort the ability togeneralize to the target population. In this study we propose an approach forestimating the finite population mean of a longitudinal outcome conditioning onbeing alive at a specific time point. We develop a flexible Bayesiansemi-parametric predictive estimator for population inference when longitudinalauxiliary information is known for the target population. We evaluatesensitivity of the results to untestable assumptions and further compare ourapproach to other methods used for population inference in a simulation study.The proposed approach is motivated by 15-year longitudinal data from the Betulalongitudinal cohort study. We apply our approach to estimate lifespantrajectories in episodic memory, with the aim to generalize findings to atarget population.

A Bayesian semi-parametric approach for inference on the population partly conditional mean from longitudinal data with dropout