A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.
A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.
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从辍学的纵向数据中推断人口部分平均值的贝叶斯半参数方法。
DOI：
10.1093/biostatistics/kxab012
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发表时间：
2023-04-14
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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.
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