Bias Analysis for Logistic Regression with a Misclassified Multi-categorical Exposure

View/ Open
Date
2012-03-22Author
Liu, Yaqing
Type
ThesisDegree Level
MastersMetadata
Show full item recordAbstract
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.
Degree
Master of Science (M.Sc.)Department
School of Public HealthProgram
BiostatisticsSupervisor
Liu, JuxinCommittee
Lix, Lisa; Lawson, Josh; Muhajarine, NazeemCopyright Date
February 2012Subject
misclassification
multi-categorical misclassification
asymptotic bias
large-sample theory
odds ratio
logistic regression