Statistics for Epidemiology

ISBN-10: 1584884339
ISBN-13: 9781584884330
Edition: 2004
List price: $109.95 Buy it from $86.76
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Description: 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  More...

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Book details

List price: $109.95
Copyright year: 2004
Publisher: CRC Press LLC
Publication date: 8/26/2003
Binding: Hardcover
Pages: 350
Size: 6.25" wide x 9.25" long x 1.00" tall
Weight: 1.386
Language: English

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
Study design
Binary outcome data
Causality
Overview
Caution: what is not covered
Comments and further reading
Measures of Disease Occurrence
Prevalence and incidence
Disease rates
The hazard function
Comments and further reading
Problems
The Role of Probability in Observational Studies
Simple random samples
Probability and the incidence proportion
Inference based on an estimated probability
Conditional probabilities
Independence of two events
Example of conditional probabilities--Berkson's bias
Comments and further reading
Problems
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
Comments and further reading
Problems
Study Designs
Population-based studies
Example--mother's marital status and infant birthweight
Exposure-based sampling--cohort studies
Disease-based sampling--case-control studies
Key variants of the case-control design
Risk-set sampling of controls
Case-cohort studies
Comments and further reading
Problems
Assessing Significance in a 2 x 2 Table
Population-based designs
Role of hypothesis tests and interpretation of p-values
Cohort designs
Case-control designs
Comparison of the study designs
Comments and further reading
Alternative formulations of the X[superscript 2] test statistic
When is the sample size too small to do a X[superscript 2] test?
Problems
Estimation and Inference for Measures of Association
The odds ratio
Sampling distribution of the odds ratio
Confidence interval for the odds ratio
Example--coffee drinking and pancreatic cancer
Small sample adjustments for estimators of the odds ratio
The relative risk
Example--coronary heart disease in the Western Collaborative Group Study
The excess risk
The attributable risk
Comments and further reading
Measurement error or misclassification
Problems
Causal Inference and Extraneous Factors: Confounding and Interaction
Causal inference
Counterfactuals
Confounding variables
Control of confounding by stratification
Causal graphs
Assumptions in causal graphs
Causal graph associating childhood vaccination to subsequent health condition
Using causal graphs to infer the presence of confounding
Controlling confounding in causal graphs
Danger: controlling for colliders
Simple rules for using a causal graph to choose the crucial confounders
Collapsibility over strata
Comments and further reading
Problems
Control of Extraneous Factors
Summary test of association in a series of 2 X 2 tables
The Cochran-Mantel-Haenszel test
Sample size issues and a historical note
Summary estimates and confidence intervals for the odds ratio, adjusting for confounding factors
Woolf's method on the logarithm scale
The Mantel-Haenszel method
Example--the Western Collaborative Group Study: part 2
Example--coffee drinking and pancreatic cancer: part 2
Summary estimates and confidence intervals for the relative risk, adjusting for confounding factors
Example--the Western Collaborative Group Study: part 3
Summary estimates and confidence intervals for the excess risk, adjusting for confounding factors
Example--the Western Collaborative Group Study: part 4
Further discussion of confounding
How do adjustments for confounding affect precision?
An empirical approach to confounding
Comments and further reading
Problems
Interaction
Multiplicative and additive interaction
Multiplicative interaction
Additive interaction
Interaction and counterfactuals
Test of consistency of association across strata
The Woolf method
Alternative tests of homogeneity
Example--the Western Collaborative Group Study: part 5
The power of the test for homogeneity
Example of extreme interaction
Comments and further reading
Problems
Exposures at Several Discrete Levels
Overall test of association
Example--coffee drinking and pancreatic cancer: part 3
A test for trend in risk
Qualitatively ordered exposure variables
Goodness of fit and nonlinear trends 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
Comments and further reading
Problems
Regression Models Relating Exposure to Disease
Some introductory regression models
The linear model
Pros and cons of the linear model
The log linear model
The probit model
The simple logistic regression model
Interpretation of logistic regression parameters
Simple examples of the models with a binary exposure
Multiple logistic regression model
The use of indicator variables for discrete exposures
Comments and further reading
Problems
Estimation of Logistic Regression Model Parameters
The likelihood function
The likelihood function based on a logistic regression model
Properties of the log likelihood function and the maximum likelihood estimate
Null hypotheses that specify more than one regression coefficient
Example--the Western Collaborative Group Study: part 7
Logistic regression with case-control data
Example--coffee drinking and pancreatic cancer: part 5
Comments and further reading
Problems
Confounding and Interaction within Logistic Regression Models
Assessment of confounding using logistic regression models
Example--the Western Collaborative Group Study: part 8
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
Centering independent variables
Fitting quadratic models
Restrictions on effective use of maximum likelihood techniques
Comments and further reading
Measurement error
Missing data
Problems
Goodness of Fit Tests for Logistic Regression Models and Model Building
Choosing the scale of an exposure variable
Using ordered categories to select exposure scale
Alternative strategies
Model building
Goodness of fit
The Hosmer-Lemeshow test
Comments and further reading
Problems
Matched Studies
Frequency matching
Pair matching
Mantel-Haenszel techniques applied to pair-matched data
Small sample adjustment for odds ratio estimator
Example--pregnancy and spontaneous abortion in relation to coronary heart disease in women
Confounding and interaction effects
Assessing interaction effects of matching variables
Possible confounding and interactive effects due to nonmatching variables
The logistic regression model for matched data
Example--pregnancy and spontaneous abortion in relation to coronary heart disease in women: part 2
Example--the effect of birth order on respiratory distress syndrome in twins
Comments and further reading
When can we break the match?
Final thoughts on matching
Problems
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?
Time-dependent exposures
Differential loss to follow-up
Comments and further reading
Problems
Epilogue: The Examples
References
Glossary of Common Terms and Abbreviations
Index

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