Predictors of attrition in a longitudinal population-based study of aging.

ABSTRACT:

Background

Longitudinal studies predictably experience non-random attrition over time. Among older adults, risk factors for attrition may be similar to risk factors for outcomes such as cognitive decline and dementia, potentially biasing study results.

Objective

To characterize participants lost to follow-up which can be useful in the study design and interpretation of results.

Methods

In a longitudinal aging population study with 10 years of annual follow-up, we characterized the attrited participants (77%) compared to those who remained in the study. We used multivariable logistic regression models to identify attrition predictors. We then implemented four machine learning approaches to predict attrition status from one wave to the next and compared the results of a

SUBMITTER: Jacobsen E 

PROVIDER: S-EPMC7572515 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature