A Bayesian natural cubic B-spline varying coefficient method for non-ignorable dropout.

ABSTRACT:

Background

Dropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account for dropout and introduces a Bayesian semi-parametric varying coefficient model for exponential family longitudinal data with non-ignorable dropout.

Methods

To demonstrate these methods, we present results from a simulation study and estimate the impact of drug use on longitudinal CD4 ⁺ T cell count and viral load suppression in the Women's Interagency HIV Study. Sensitivity analyses are performed to consider the impact of model assumptions on inference. We compare results between our semi-parametric method and pa

SUBMITTER: Moore CM 

PROVIDER: S-EPMC7539484 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature