Bias in Odds Ratios From Logistic Regression Methods With Sparse Data Sets
Abstract
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...
Paper Details
Title
Bias in Odds Ratios From Logistic Regression Methods With Sparse Data Sets
Published Date
Jun 5, 2023
Journal
Volume
33
Issue
6
Pages
265 - 275
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