Bendix Carstensen is a Senior Statistician in Clinical Epidemiology at the Steno Diabetes Center, Gentofte and an External Lecturer at the Department of Biostatistics, University of Copenhagen, Denmark. His expertise and interests are chiefly in the areas of Biostatistics, Epidemiology/Public Health and Diabetes Epidemiology.

This volume will be useful to epidemiologists who are looking for a quick reference guide on how to practically use R in epidemiological research, particularly the Epi and survival packages.

• Preface Introduction 1: Using R 2: Measures of disease occurrence 3: Prevalence data- models, likelihood and binomial regression 4: Regression models 5: Analysis of follow-up data 6: Parametrization and prediction of rates 7: Case-control and case-cohort studies 8: Survival analysis 9: Do not group quantitative variables

Bendix Carstensen is a Senior Statistician in Clinical Epidemiology at the Steno Diabetes Center, Gentofte and an External Lecturer at the Department of Biostatistics, University of Copenhagen, Denmark. His expertise and interests are chiefly in the areas of Biostatistics, Epidemiology/Public Health and Diabetes Epidemiology.

This volume will be useful to epidemiologists who are looking for a quick reference guide on how to practically use R in epidemiological research, particularly the Epi and survival packages.

• Preface Introduction 1: Using R 2: Measures of disease occurrence 3: Prevalence data- models, likelihood and binomial regression 4: Regression models 5: Analysis of follow-up data 6: Parametrization and prediction of rates 7: Case-control and case-cohort studies 8: Survival analysis 9: Do not group quantitative variables

Bendix Carstensen is a Senior Statistician in Clinical Epidemiology at the Steno Diabetes Center, Gentofte and an External Lecturer at the Department of Biostatistics, University of Copenhagen, Denmark. His expertise and interests are chiefly in the areas of Biostatistics, Epidemiology/Public Health and Diabetes Epidemiology.

This volume will be useful to epidemiologists who are looking for a quick reference guide on how to practically use R in epidemiological research, particularly the Epi and survival packages.

• Preface Introduction 1: Using R 2: Measures of disease occurrence 3: Prevalence data- models, likelihood and binomial regression 4: Regression models 5: Analysis of follow-up data 6: Parametrization and prediction of rates 7: Case-control and case-cohort studies 8: Survival analysis 9: Do not group quantitative variables