Measuring effects of medication adherence on time-varying health outcomes using Bayesian dynamic linear models.

ABSTRACT: One of the most significant barriers to medication treatment is patients' non-adherence to a prescribed medication regimen. The extent of the impact of poor adherence on resulting health measures is often unknown, and typical analyses ignore the time-varying nature of adherence. This article develops a modeling framework for longitudinally recorded health measures modeled as a function of time-varying medication adherence. Our framework, which relies on normal Bayesian dynamic linear models (DLMs), accounts for time-varying covariates such as adherence and non-dynamic covariates such as baseline health characteristics. Standard inferential procedures for DLMs are inefficient when faced with infrequent and irregularly recorded response data. We develop an approach that relies on factoring t

SUBMITTER: Campos LF 

PROVIDER: S-EPMC8562069 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature