Analysis of Longitudinal Studies in Epidemiology

Klappentext Aimed at a nontechnical audience, with intuitive explanations instead of mathematical derivations, Analysis of Longitudinal Studies in Epidemiology covers a wide range of topics that include Poisson regression, survival analysis, repeated measure, clustered data, longitudinal observations, and generalized linear models such as logistic regression. The book illustrates examples through extensive case studies, placing particular emphasis on the extension of simple cross-sectional methods and exploring how they can be adapted to longitudinal settings. It also explains the importance of time-dependent confounding and introduces methods including inverse probability weighted estimators. Zusammenfassung Covers a range of topics that include Poisson regression, survival analysis, repeated measure, clustered data, longitudinal observations, and generalized linear models such as logistic regression. This book explains the importance of time-dependent confounding and introduces methods including inverse probability weighted estimators. Inhaltsverzeichnis Introduction. Longitudinal Studies of Disease Incidence. Models for Survival Data. Longitudinal Data Models. Mixed Effects Models. Causal Inference in Longitudinal Studies. The Probability Inverse Weighted Approach to Causal Inference. Ecological Longitudinal Studies. Cross-Sectional Data on Event Times.

Résumé
Covers a range of topics that include Poisson regression, survival analysis, repeated measure, clustered data, longitudinal observations, and generalized linear models such as logistic regression. This book explains the importance of time-dependent confounding and introduces methods including inverse probability weighted estimators.

Contenu

Introduction. Longitudinal Studies of Disease Incidence. Models for Survival Data. Longitudinal Data Models. Mixed Effects Models. Causal Inference in Longitudinal Studies. The Probability Inverse Weighted Approach to Causal Inference. Ecological Longitudinal Studies. Cross-Sectional Data on Event Times.