  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. 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
semi-parametric predictive estimator for population inference when longitudinal
auxiliary information is known for the target population. We evaluate
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 estimate lifespan
trajectories in episodic memory, with the aim to generalize findings to a
target population.
