1st Edition

Statistics for Epidemiology

By Nicholas P. Jewell Copyright 2004
    350 Pages 47 B/W Illustrations
    by Chapman & Hall

    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.

    INTRODUCTION
    Disease Processes
    Statistical Approaches to Epidemiological Data
    Causality
    Overview
    MEASURES OF DISEASE OCCURRENCE
    Prevalence and Incidence
    Disease rates
    THE ROLE OF PROBABILITY IN OBSERVATIONAL STUDIES
    Simple Random Samples
    Probability and the Incidence Proportion
    Inference Based on an Estimated Probability
    Conditional Probabilities
    Example of Conditional Probabilities-Berkson's Bias
    MEASURES OF DISEASE-EXPOSURE ASSOCIATION
    Relative Risk
    Odds Ratio
    The Odds Ratio as an Approximation to the Relative Risk
    Symmetry of Roles of Disease and Exposure in the Odds Ratio
    Relative Hazard
    Excess Risk
    Attributable Risk
    STUDY DESIGNS
    Population-Based Studies
    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
    Population-Based Designs
    Cohort Designs
    Case-Control Designs
    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
    Causal Inference
    Causal Graphs
    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
    INTERACTION
    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
    Model Building
    Goodness of Fit
    MATCHED STUDIES
    Frequency Matching
    Pair Matching
    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
    REFERENCES
    GLOSSARY OF COMMON TERMS AND ABBREVIATIONS
    INDEX

    Each chapter also contains sections of Problems and Further Reading.

    Biography

    Nicholas P. Jewell

    "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