Description
Book SynopsisEpidemiologic Research Principles and Quantitative Methods DavidG. Kleinbaum, Ph.D. Lawrence L. Kupper. Ph.D. Hal Morgenstern,Ph.D.
Epidemiologic Research covers the principles and methodsof planning, analysis and interpretation of epidemiologic researchstudies. It supplies the applied researcher with the mostup-to-date methodological thought and practice. Specifically, thebook focuses on quantitative (including statistical) issues arisingfrom epidemiologic investigations, as well as on the questions ofstudy design, measurement and validity. EpidemiologicResearch emphasizes practical techniques, procedures andstrategies. It presents them through a unified approach whichfollows the chronology of issues that arise during theinvestigation of an epidemic. The book''s viewpoint ismultidisciplinary and equally useful to the epidemiologicresearcher and to the biostatistician. Theory is supplemented bynumerous examples, exercises and applications. Full solutions aregiven to all exerci
Table of ContentsKey Issues in Epidemiologic Research: An Overview.
OBJECTIVES AND METHODS OR EPIDEMIOLOGIC RESEARCH.
Fundamentals of Epidemiologic Research.
Types of Epidemiologic Research.
Design Options in Observational Studies.
Typology of Observational Study Designs.
Measures of Disease Frequency: Incidence.
Other Measures of Disease Frequency.
Measures of Association.
Measures of Potential Impact and Summary of the Measures.
VALIDITY OF EPIDEMIOLOGIC RESEARCH.
Validity: General Considerations.
Selection Bias.
Information Bias.
Confounding.
Confounding Involving Several Risk Factors.
PRINCIPLES AND PROCEDURES OF EPIDEMIOLOGIC ANALYSIS.
Statistical Inferences About Effect Measures: SimpleAnalysis.
Overview of Options for Control of Extraneous Factors.
Stratified Analysis.
Matching in Epidemiologic Studies.
Interaction, Effect Modification, and Synergism.
Modeling: Theoretical Considerations.
Modeling: Analysis Strategy.
Applications of Modeling with No Interaction.
Applications of Logistic Regression with Interaction, UsingUnconditional ML Estimation.
Applications of Modeling: Conditional Likelihood Estimation.
Appendices.
Index.
