Bias Analysis for Logistic Regression with a Misclassified Multi-categorical Exposure
Date
2012-03-22
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
Type
Degree Level
Masters
Abstract
In epidemiological studies, it is one common issue that the collected data may not be
perfect due to technical and/or nancial di culties in reality. It is well known that ignoring
such imperfections may lead to misleading inference results (e.g., fail to detect the actual
association between two variables). Davidov et al.(2003) have studied asymptotic biases
caused by misclassi cation in a binary exposure in a logistic regression context. The aim of
this thesis is to extend the work of Davidov et al. to a multi-categorical scenario. I examine
asymptotic biases on regression coe cients of a logistic regression model when the multicategorical
exposure is subject to misclassi cation. The asymptotic results may provide
insight guide for large scale studies when considering whether bias corrections would be
necessary. To better understand the asymptotic results, I also conduct some numerical
examples and simulation studies.
Description
Keywords
misclassification, multi-categorical misclassification, asymptotic bias, large-sample theory, odds ratio, logistic regression
Citation
Degree
Master of Science (M.Sc.)
Department
School of Public Health
Program
Biostatistics