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Statistics for Epidemiology (CRC/C&H Texts in Statistical Science (Hardcover), nr. 58)

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en Limba Engleză Hardback – 26 Aug 2003
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.
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Specificații

ISBN-13: 9781584884330
ISBN-10: 1584884339
Pagini: 350
Ilustrații: 47 black & white illustrations, 116 black & white tables
Dimensiuni: 154 x 243 x 23 mm
Greutate: 0.63 kg
Ediția: New.
Editura: CRC Press
Seriile Chapman & Hall/CRC Texts in Statistical Science , CRC/C&H Texts in Statistical Science (Hardcover)


Public țintă

Graduate students of epidemiology and public health, health professionals, epidemiologists and community health workers, applied statisticians, biostatisticians

Cuprins

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.