Handbook of Statistical Methods for Case-Control Studies

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.

"This book is essential reading and reference for any statistical methodologist with interest in case-controlstudies...This book is a very good place to start on the next leg of our statistical journey in this field."~Nicholas P. Jewell, ISCB Newsletter" . . . as a handbook, it is designed to address specific methodological issues, more like a toolbox. And this is done well. All chapters come with an introduction and a worked example using sample data, with ample reference to further details. Occasional chapters on unconventional study designs provide food for thought. Overall, the book is well written and very comprehensive; it provides help for many situations, and for situations of greater complexity it points to further references."~Anika Hüsing, Biometrical Journal

• 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.