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

TL;DR

This paper introduces a Bayesian semi-parametric method for estimating population means from longitudinal data with dropout, addressing biases from non-representative samples and practice effects to improve generalizability.

Contribution

It develops a flexible Bayesian estimator for population inference that accounts for dropout and auxiliary information, with sensitivity analysis and comparison to existing methods.

Findings

The method effectively adjusts for dropout bias.

Simulation studies show improved accuracy over traditional methods.

Application to Betula data estimates lifespan memory trajectories.

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. 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.