Statistics for Epidemiology

Statistics for Epidemiology

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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 more

Product details

  • Hardback | 350 pages
  • 154.94 x 236.22 x 25.4mm | 657.71g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 47 black & white illustrations, 116 black & white tables
  • 1584884339
  • 9781584884330
  • 537,368

Review quote

"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 "Good points of the book are the exercises, comments and further reading at the end of each chapter, the availability of the data sets usedand the extensive discussion of confoundingthis 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 2004show more

Table of contents

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 more

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