Ørnulf Borgan, Norman Breslow, Nilanjan Chatterjee, Mitchell H. Gail, Alastair Scott, Chris J. Wild
Chapman and Hall/CRC
Published July 2, 2018
Reference - 536 Pages
ISBN 9781498768580 - CAT# K29253
Series: Chapman & Hall/CRC Handbooks of Modern Statistical Methods
For Librarians Available on Taylor & Francis eBooks >>
Handbook of Statistical Methods for Case-Control Studies is written by leading researchers in the field. It provides an in-depth treatment of up-to-date and currently developing statistical methods for the design and analysis of case-control studies, as well as a review of classical principles and methods. The handbook is designed to serve as a reference text for biostatisticians and quantitatively-oriented epidemiologists who are working on the design and analysis of case-control studies or on related statistical methods research. Though not specifically intended as a textbook, it may also be used as a backup reference text for graduate level courses.
About the Editors
Ørnulf Borgan is Professor of Statistics, University of Oslo. His book with Andersen, Gill and Keiding on counting processes in survival analysis is a world classic.
Norman E. Breslow was, at the time of his death, Professor Emeritus in Biostatistics, University of Washington. For decades, his book with Nick Day has been the authoritative text on case-control methodology.
Nilanjan Chatterjee is Bloomberg Distinguished Professor, Johns Hopkins University. He leads a broad research program in statistical methods for modern large scale biomedical studies.
Mitchell H. Gail is a Senior Investigator at the National Cancer Institute. His research includes modeling absolute risk of disease, intervention trials, and statistical methods for epidemiology.
Alastair Scott was, at the time of his death, Professor Emeritus of Statistics, University of Auckland. He was a major contributor to using survey sampling methods for analyzing case-control data.
Chris J. Wild is Professor of Statistics, University of Auckland. His research includes nonlinear regression and methods for fitting models to response-selective data.
Introduction. Introduction. Origins. Classical Case-Control Studies. Design issues in case-control studies. Basic concepts and methods of analysis. Matched samples. Beyond logistic regression. Small sample methods. Multiple case or control groups. Power and sample size. Causal inference. Misclassification and measurement error. Analysis of secondary phenotype under case-control design. Sampling from a Defined Cohort. Two and three (or multi) phase sampling designs. Calibration and estimation of sampling weights. Maximum likelihood. Re-use of case-control samples. Misspecification. Case-control studies with complex sampling. Cohort sampling for time to event data. Case-cohort designs and analyses. Design options and partial likelihood analyses of nested case-control data. Inverse probability weighting in nested case-control studies. Multiple imputation. Maximum likelihood. Self controlled case series. Genetic Epidemiology. Basic design and association analysis of population-based case-control studies. Analysis of gene-environment interactions. Screening methods for detecting genetic association and interactions under case-control design. Analysis of family-based case-control studies. Fitting mixed model to case-control genome-wide association studies. Analysis of secondary phenotype under case-control design.