Comparisons of conditional logistic regression vs. a discriminant function approach in a case-control study where matching is performed Open Access
Li, Ruoxing (Spring 2020)
Abstract
The logistic regression model has been widely used for estimating adjusted odds ratio associated with a binary outcome in case-control study. When matching is involved, conditional logistic regression is more commonly used to estimate the odds ratio corresponding to a continuous predictor as an alternative to standard unconditional model to decrease the bias caused by sparse data. In this thesis the discriminant function approach is suggested to generate closed-form estimators, especially under conditions involving few or small matched sets. The application of this approach, which given a multiple regression model form with the continuous predictor of interest on the outcome, includes fixed intercept effects for each matched set. It is demonstrated that the estimator based on discriminant function approach outperform the usual maximum likelihood estimator from logistic regression based on our simulation works and examples. The advantages have seen in reducing bias and width of CI for odds ratio, as well as generating reliable estimator under separation situations where logistic regression fails. Potential improvements for this study are also talked in the end of the article.
Table of Contents
Introduction 1
Methods 3
---Standard logistic regression 4
---Conditional logistic regression for matched case-control studies 5
---Discriminant function approach 7
Examples 10
Simulation Studies and Results 14
Discussion 19
Reference 22
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