Chapter

Advanced Statistical Analysis

Harvey Checkoway, Neil Pearce and David Kriebel

in Research Methods in Occupational Epidemiology

Second edition

Published in print March 2004 | ISBN: 9780195092424
Published online September 2009 | e-ISBN: 9780199864553 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780195092424.003.0009

Series: Monographs in Epidemiology and Biostatistics

Advanced Statistical Analysis

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Stratified analyses may not be feasible if there are multiple exposure categories or two or more than two or three confounders. In this situation, multiple regression methods are required. This chapter presents an overview of the methods that are used for analyzing occupational epidemiology data. It begins with a presentation of the basic multiple linear regression model used when the outcome of interest in measured as a continuous variable. This is followed by a presentation of generalized estimating equation (GEE) methods in the context of repeated measures analysis. The general form of the log-linear model is then introduced. The specific forms of Poisson regression (for cohort studies), the Cox proportional hazards model (for survival studies), and logistic regression (for case-control studies) are then defined and illustrated with occupational epidemiology examples. Various aspects of model specification are considered, including variable specification, estimation of joint effects, exposure-response estimation, and regression diagnostics.

Keywords: Cox proportional hazards model; data analysis; generalize estimating equations; GEE; log-linear models; logistic regression; Poisson regression; regression modeling

Chapter.  12763 words.  Illustrated.

Subjects: Public Health and Epidemiology ; Medical Statistics and Methodology

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