Assumes only a moderate level of mathematical background, but goes well beyond being a "cookbook"
Includes recent material that highlights the ideas that link counterfactuals and causal inference to the concepts of confounding
Introduces regression models for binary outcomes with matched and unmatched data, including logistic regression and the Cox model
Develops the intuition needed to understand why and when certain methods are used
Provides datasets, solutions to the exercises, and Stata code at http://www.crcpress.com/product/isbn/9781584884330.
Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes."
Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches.
Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies.
Table of Contents
Statistical Approaches to Epidemiological Data
MEASURES OF DISEASE OCCURRENCE
Prevalence and Incidence
THE ROLE OF PROBABILITY IN OBSERVATIONAL STUDIES
Simple Random Samples
Probability and the Incidence Proportion
Inference Based on an Estimated Probability
Example of Conditional Probabilities-Berkson's Bias
MEASURES OF DISEASE-EXPOSURE ASSOCIATION
The Odds Ratio as an Approximation to the Relative Risk
Symmetry of Roles of Disease and Exposure in the Odds Ratio
Exposure-Based Sampling-Cohort Studies
Disease-Based Sampling-Case-Control Studies
Key Variants of the Case-Control Design
ASSESSING SIGNIFICANCE IN A 2 x 2 TABLE
ESTIMATION AND INFERENCE FOR MEASURES OF ASSOCIATION
The Odds Ratio
The Relative Risk
The Excess Risk
The Attributable Risk
CAUSAL INFERENCE AND EXTRANEOUS FACTORS: CONFOUNDING AND INTERACTION
Controlling Confounding in Causal Graphs
Collapsibility over Strata
CONTROL OF EXTRANEOUS FACTORS
Summary Test of Association in a Series of 2 x 2 Tables
Summary Estimates and Confidence Intervals for the Odds Ratio, Adjusting for confounding Factors
Summary Estimates and Confidence Intervals for the Relative Risk, Adjusting for Confounding Factors
Summary Estimates and Confidence Intervals for the Excess Risk, Adjusting for Confounding Factors
Further Discussion of Confounding
Multiplicative and Additive Interaction
Interaction and Counterfactuals
Test of Consistency of Association across Strata
Example of Extreme Interaction
EXPOSURES AT SEVERAL DISCRETE LEVELS
Overall Test of Association
Example-Coffee Drinking and Pancreatic Cancer: Part 3
A Test for Trend in Risk
Example-The Western Collaborative Group Study: Part 6
Example-Coffee Drinking and Pancreatic Cancer: Part 4
Adjustment for Confounding, Exact Tests, and Interaction
REGRESSION MODELS RELATING EXPOSURE TO DISEASE
Some Introductory Regression Models
The Log Linear Model
The Probit Model
The Simple Logistic Regression Model
Simple Examples of the Models with a Binary Exposure
Multiple Logistic Regression Model
ESTIMATION OF LOGISTIC REGRESSION MODEL PARAMETERS
The Likelihood Function
Example-The Western Collaborative Group Study: Part 7
Logistic Regression with Case-Control Data
Example-Coffee Drinking and Pancreatic Cancer: Part 5
CONFOUNDING AND INTERACTION WITHIN LOGISTIC REGRESSION MODELS
Assessment of Confounding Using Logistic Regression Models
Introducing Interaction into the Multiple Logistic Regression Model
Example-Coffee Drinking and Pancreatic Cancer: Part 6
Example-The Western Collaborative Group Study: Part 9
Collinearity and Centering Variables
Restrictions on Effective Use of Maximum Likelihood Techniques
GOODNESS OF FIT TESTS FOR LOGISTIC REGRESSION MODELS AND MODEL BUILDING
Choosing the Scale of an Exposure Variable
Goodness of Fit
Example-Pregnancy and Spontaneous Abortion in Relation to Coronary Heart Disease in Women
Confounding and Interaction Effects
The Logistic Regression Model for Matched Data
Example-The Effect of Birth Order on Respiratory Distress Syndrome in Twins
ALTERNATIVES AND EXTENSIONS TO THE LOGISTIC REGRESSION MODEL
Flexible Regression Model
Beyond Binary Outcomes and Independent Observations
Introducing General Risk Factors into Formulation of the Relative Hazard-The Cox Model
Fitting the Cox Regression Model
When Does Time at Risk Confound an Exposure-Disease Relationship?
EPILOGUE: THE EXAMPLES
GLOSSARY OF COMMON TERMS AND ABBREVIATIONS
Each chapter also contains sections of Problems and Further Reading.
"Jewell's book can certainly be included in any group of useful books on statistics in epidemiology. It actually might be the one with which I would start."
-Technometrics, February 2005, Vol. 47, No. 1
"This is a useful and thought-provoking book written by an expert in the field, providing a very valuable supplement to more introductory texts as well as a guide to more advanced sources. "
-Journal of the Royal Statistics Society
"…It is a good companion text … The numerous worked examples and references to further reading at the end of each chapter are very good … I would find the book a useful reference for the teacher of statistical methods for epidemiology …"
-Statistics in Medicine, 2005
"Good points of the book are the exercises, comments and further reading at the end of each chapter, the availability of the data sets used … and the extensive discussion of confounding … this is a good, well-written piece of work."
-Pharmaceutical Statistics, 2004
"This book is excellent; a real breakthrough in texts on statistics in epidemiology, especially in its attention to causation and bias."
-Sander Greenland, Department of Epidemiology, UCLA
"Using examples, this experienced statistician identifies scientific issues and clearly links them to statistical approaches. Statistical theory and formality are grounded in manageable yet realistic examples. Coverage includes the basics and important topics such as measurement error and causal analysis. The book has excellent references, an informative index and glossary."
-ISI Short Book Reviews, August 2004
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