# Statistics for Epidemiology

#### By **Nicholas P. Jewell**

#### Series Editor: **Chris Chatfield**, **Martin A. Tanner**, **Martin A. Tanner**

Chapman and Hall/CRC – 2004 – 352 pages

Chapman and Hall/CRC – 2004 – 352 pages

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.

"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

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.

Name: Statistics for Epidemiology (Hardback) – Chapman and Hall/CRC

Description: By Nicholas P. JewellSeries Editor: Chris Chatfield, Martin A. Tanner, Martin A. Tanner. 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...

Categories: Epidemiology, Statistics for the Biological Sciences