Bias in Odds Ratios From Logistic Regression Methods With Sparse Data Sets.

ABSTRACT:

Background

Logistic regression models are widely used to evaluate the association between a binary outcome and a set of covariates. However, when there are few study participants at the outcome and covariate levels, the models lead to bias of the odds ratio (OR) estimated using the maximum likelihood (ML) method. This bias is known as sparse data bias, and the estimated OR can yield impossibly large values because of data sparsity. However, this bias has been ignored in most epidemiological studies.

Methods

We review several methods for reducing sparse data bias in logistic regression. The primary aim is to evaluate the Bayesian methods in comparison with the classical methods, such as the ML, Firth's, and exact methods using a simulation study. We also apply these methods t

SUBMITTER: Gosho M 

PROVIDER: S-EPMC10165217 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature