Comparisons of conditional logistic regression vs. a discriminant function approach in a case-control study where matching is performed Öffentlichkeit

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

 

About this Master's Thesis

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School	Rollins School of Public Health
Department	Biostatistics
Subfield / Discipline	Biostatistics - MPH & MSPH
Degree	M.S.P.H.
Submission	Master's Thesis
Language	English
Research Field	Statistics
Stichwort	Discriminant function approach. Logistic regression
Committee Chair / Thesis Advisor	Lyles, Robert H., Emory University
Committee Members	Hanfelt, John, Emory University

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