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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 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.
Highlights
Studies of lifespan trajectories in memory using longitudinal data present numerous methodological challenges including highly nonrepresentative samples, due to selective study enrollment and attrition, and practice effects, which results in improved or maintained performance due to familiarity with the testM
We evaluate the sensitivity of the results to untestable assumptions on missing not at random (MNAR) dropout and practice effects (PEs) and further compare our approach to other methods used for population inference in a simulation study
We propose a Bayesian semiparametric modeling approach based on Bayesian Additive Regression Trees (BART; Chipman and others, 2010) for the working model in (3.2) using the observed data and the sensitivity parameters
Summary
Studies of lifespan trajectories in memory using longitudinal data present numerous methodological challenges including highly nonrepresentative samples, due to selective study enrollment and attrition, and practice effects, which results in improved or maintained performance due to familiarity with the testM. Studies of lifespan trajectories in memory using longitudinal data present numerous methodological challenges including highly nonrepresentative samples, due to selective study enrollment and attrition, and practice effects, which results in improved or maintained performance due to familiarity with the test. Common statistical approaches for handling PEs include: ignoring, specifying an indicator for the first assessment, or modeling a linear trend (Vivot and others, 2016). These approaches remain controversial due to, for example, the strong assumption that the practice gains are the same across the age range or the difficulties in separating effects of within-person change from PEs (Weuve and others, 2015; Hoffman and others, 2011)
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