Statistics for epidemiology / Nicholas P. Jewell.

Format:

Book

Contributor:

Jewell, Nicholas P., 1952-

Series:

Texts in statistical science

Texts in statistical science series

Language:

English

Subjects (All):

Epidemiology--Statistical methods.

Epidemiology.

Epidemiology--statistics & numerical data.

Epidemiologic Methods.

Statistics as Topic--methods.

Medical Subjects:

Epidemiology--statistics & numerical data.

Epidemiologic Methods.

Statistics as Topic--methods.

Physical Description:

xiv, 333 pages : illustrations ; 25 cm.

Place of Publication:

Boca Raton, Fla. : Chapman & Hall/CRC, [2004]

Summary:

This graduate textbook explains the collection and analysis of data that speak to relationships between the occurrence of diseases and various descriptive characteristics of individuals in a population.

Contents:

1.1 Disease processes 1

1.2 Statistical approaches to epidemiological data 2

1.2.2 Binary outcome data 4

1.3 Causality 5

2 Measures of Disease Occurrence 9

2.1 Prevalence and incidence 9

2.2 Disease rates 12

2.2.1 The hazard function 13

3 The Role of Probability in Observational Studies 19

3.1 Simple random samples 20

3.2 Probability and the incidence proportion 21

3.3 Inference based on an estimated probability 22

3.4 Conditional probabilities 24

3.4.1 Independence of two events 26

3.5 Example of conditional probabilities

Berkson's bias 26

4 Measures of Disease-Exposure Association 31

4.1 Relative risk 31

4.2 Odds ratio 32

4.3 The odds ratio as an approximation to the relative risk 33

4.4 Symmetry of roles of disease and exposure in the odds ratio 34

4.5 Relative hazard 35

4.6 Excess risk 37

4.7 Attributable risk 38

5 Study Designs 43

5.1 Population-based studies 45

5.1.1 Example

mother's marital status and infant birthweight 46

5.2 Exposure-based sampling

cohort studies 47

5.3 Disease-based sampling

case-control studies 48

5.4 Key variants of the case-control design 50

5.4.1 Risk-set sampling of controls 51

5.4.2 Case-cohort studies 53

6 Assessing Significance in a 2 x 2 Table 59

6.1 Population-based designs 59

6.1.1 Role of hypothesis tests and interpretation of p-values 61

6.2 Cohort designs 62

6.3 Case-control designs 64

6.3.1 Comparison of the study designs 65

6.4.1 Alternative formulations of the X[superscript 2] test statistic 69

6.4.2 When is the sample size too small to do a X[superscript 2] test? 70

7 Estimation and Inference for Measures of Association 73

7.1 The odds ratio 73

7.1.1 Sampling distribution of the odds ratio 74

7.1.2 Confidence interval for the odds ratio 77

7.1.3 Example

coffee drinking and pancreatic cancer 78

7.1.4 Small sample adjustments for estimators of the odds ratio 79

7.2 The relative risk 81

7.2.1 Example

coronary heart disease in the Western Collaborative Group Study 82

7.3 The excess risk 83

7.4 The attributable risk 84

7.5.1 Measurement error or misclassification 86

8 Causal Inference and Extraneous Factors: Confounding and Interaction 93

8.1 Causal inference 94

8.1.1 Counterfactuals 94

8.1.2 Confounding variables 99

8.1.3 Control of confounding by stratification 100

8.2 Causal graphs 102

8.2.1 Assumptions in causal graphs 105

8.2.2 Causal graph associating childhood vaccination to subsequent health condition 106

8.2.3 Using causal graphs to infer the presence of confounding 107

8.3 Controlling confounding in causal graphs 109

8.3.1 Danger: controlling for colliders 109

8.3.2 Simple rules for using a causal graph to choose the crucial confounders 111

8.4 Collapsibility over strata 112

9 Control of Extraneous Factors 123

9.1 Summary test of association in a series of 2 X 2 tables 123

9.1.1 The Cochran-Mantel-Haenszel test 125

9.1.2 Sample size issues and a historical note 128

9.2 Summary estimates and confidence intervals for the odds ratio, adjusting for confounding factors 128

9.2.1 Woolf's method on the logarithm scale 129

9.2.2 The Mantel-Haenszel method 130

9.2.3 Example

the Western Collaborative Group Study: part 2 131

9.2.4 Example

coffee drinking and pancreatic cancer: part 2 133

9.3 Summary estimates and confidence intervals for the relative risk, adjusting for confounding factors 134

9.3.1 Example

the Western Collaborative Group Study: part 3 135

9.4 Summary estimates and confidence intervals for the excess risk, adjusting for confounding factors 136

9.4.1 Example

the Western Collaborative Group Study: part 4 137

9.5 Further discussion of confounding 138

9.5.1 How do adjustments for confounding affect precision? 138

9.5.2 An empirical approach to confounding 142

10 Interaction 147

10.1 Multiplicative and additive interaction 148

10.1.1 Multiplicative interaction 148

10.1.2 Additive interaction 149

10.2 Interaction and counterfactuals 150

10.3 Test of consistency of association across strata 152

10.3.1 The Woolf method 153

10.3.2 Alternative tests of homogeneity 155

10.3.3 Example

the Western Collaborative Group Study: part 5 156

10.3.4 The power of the test for homogeneity 158

10.4 Example of extreme interaction 160

11 Exposures at Several Discrete Levels 165

11.1 Overall test of association 165

11.2 Example

coffee drinking and pancreatic cancer: part 3 167

11.3 A test for trend in risk 167

11.3.1 Qualitatively ordered exposure variables 169

11.3.2 Goodness of fit and nonlinear trends in risk 170

11.4 Example

the Western Collaborative Group Study: part 6 171

11.5 Example

coffee drinking and pancreatic cancer: part 4 173

11.6 Adjustment for confounding, exact tests, and interaction 175

12 Regression Models Relating Exposure to Disease 179

12.1 Some introductory regression models 181

12.1.1 The linear model 181

12.1.2 Pros and cons of the linear model 183

12.2 The log linear model 183

12.3 The probit model 184

12.4 The simple logistic regression model 186

12.4.1 Interpretation of logistic regression parameters 187

12.5 Simple examples of the models with a binary exposure 188

12.6 Multiple logistic regression model 190

12.6.1 The use of indicator variables for discrete exposures 191

13 Estimation of Logistic Regression Model Parameters 199

13.1 The likelihood function 199

13.1.1 The likelihood function based on a logistic regression model 201

13.1.2 Properties of the log likelihood function and the maximum likelihood estimate 204

13.1.3 Null hypotheses that specify more than one regression coefficient 206

13.2 Example

the Western Collaborative Group Study: part 7 207

13.3 Logistic regression with case-control data 212

13.4 Example

coffee drinking and pancreatic cancer: part 5 215

14 Confounding and Interaction within Logistic Regression Models 221

14.1 Assessment of confounding using logistic regression models 221

14.1.1 Example

the Western Collaborative Group Study: part 8 223

14.2 Introducing interaction into the multiple logistic regression model 225

14.3 Example

coffee drinking and pancreatic cancer: part 6 227

14.4 Example

the Western Collaborative Group Study: part 9 230

14.5 Collinearity and centering variables 230

14.5.1 Centering independent variables 233

14.5.2 Fitting quadratic models 233

14.6 Restrictions on effective use of maximum likelihood techniques 235

14.7.1 Measurement error 237

14.7.2 Missing data 237

15 Goodness of Fit Tests for Logistic Regression Models and Model Building 243

15.1 Choosing the scale of an exposure variable 243

15.1.1 Using ordered categories to select exposure scale 244

15.1.2 Alternative strategies 245

15.2 Model building 246

15.3 Goodness of fit 250

15.3.1 The Hosmer-Lemeshow test 252

16 Matched Studies 257

16.1 Frequency matching 257

16.2 Pair matching 258

16.2.1 Mantel-Haenszel techniques applied to pair-matched data 262

16.2.2 Small sample adjustment for odds ratio estimator 264

16.3 Example

pregnancy and spontaneous abortion in relation to coronary heart disease in women 264

16.4 Confounding and interaction effects 265

16.4.1 Assessing interaction effects of matching variables 265

16.4.2 Possible confounding and interactive effects due to nonmatching variables 266

16.5 The logistic regression model for matched data 269

16.5.1 Example

pregnancy and spontaneous abortion in relation to coronary heart disease in women: part 2 271

16.6 Example

the effect of birth order on respiratory distress syndrome in twins 274

16.7.1 When can we break the match? 277

17 Alternatives and Extensions to the Logistic Regression Model 285

17.1 Flexible regression model 285

17.2 Beyond binary outcomes and independent observations 289

17.3 Introducing general risk factors into formulation of the relative hazard

the Cox model 290

17.4 Fitting the Cox regression model 293

17.5 When does time at risk confound an exposure-disease relationship? 295

17.5.1 Time-dependent exposures 296

17.5.2 Differential loss to follow-up 296.

Notes:

Includes bibliographical references (pages [303]-310) and index.

ISBN:

1584884339

OCLC:

52127752