Statistics for Epidemiology (Chapman & Hall/CRC Texts in Statistical Science)

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Statistics for Epidemiology (Chapman & Hall/CRC Texts in Statistical Science)

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  • Chapman & Hall/CRC(2003/08発売)
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  • 製本 Hardcover:ハードカバー版/ページ数 350 p.
  • 商品コード 9781584884330
  • DDC分類 614.40727

Full 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 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.

Table of Contents

1 Introduction                                     1  (8)
1.1 Disease processes 1 (1)
1.2 Statistical approaches to epidemiological 2 (3)
data
1.2.1 Study design 3 (1)
1.2.2 Binary outcome data 4 (1)
1.3 Causality 5 (1)
1.4 Overview 5 (2)
1.4.1 Caution: what is not covered 7 (1)
1.5 Comments and further reading 7 (2)
2 Measures of Disease Occurrence 9 (10)
2.1 Prevalence and incidence 9 (3)
2.2 Disease rates 12 (3)
2.2.1 The hazard function 13 (2)
2.3 Comments and further reading 15 (1)
2.4 Problems 16 (3)
3 The Role of Probability in Observational 19 (12)
Studies
3.1 Simple random samples 20 (1)
3.2 Probability and the incidence proportion 21 (1)
3.3 Inference based on an estimated 22 (2)
probability
3.4 Conditional probabilities 24 (2)
3.4.1 Independence of two events 26 (1)
3.5 Example of conditional 26 (2)
probabilities-Berkson's bias
3.6 Comments and further reading 28 (1)
3.7 Problems 29 (2)
4 Measures of Disease-Exposure Association 31 (12)
4.1 Relative risk 31 (1)
4.2 Odds ratio 32 (1)
4.3 The odds ratio as an approximation to the 33 (1)
relative risk
4.4 Symmetry of roles of disease and exposure 34 (1)
in the odds ratio
4.5 Relative hazard 35 (2)
4.6 Excess risk 37 (1)
4.7 Attributable risk 38 (2)
4.8 Comments and further reading 40 (1)
4.9 Problems 41 (2)
5 Study Designs 43 (16)
5.1 Population-based studies 45 (2)
5.1.1 Example-mother's marital status and 46 (1)
infant birthweight
5.2 Exposure-based sampling-cohort studies 47 (1)
5.3 Disease-based sampling-case-control 48 (2)
studies
5.4 Key variants of the case-control design 50 (5)
5.4.1 Risk-set sampling of controls 51 (2)
5.4.2 Case-cohort studies 53 (2)
5.5 Comments and further reading 55 (1)
5.6 Problems 56 (3)
6 Assessing Significance in a 2 x 2 Table 59 (14)
6.1 Population-based designs 59 (3)
6.1.1 Role of hypothesis tests and 61 (1)
interpretation of p-values
6.2 Cohort designs 62 (2)
6.3 Case-control designs 64 (4)
6.3.1 Comparison of the study designs 65 (3)
6.4 Comments and further reading 68 (3)
6.4.1 Alternative formulations of the 2 69 (1)
test statistic
6.4.2 When is the sample size too small to 70 (1)
do a x2 test?
6.5 Problems 71 (2)
7 Estimation and Inference for Measures of 73 (20)
Association
7.1 The odds ratio 73 (8)
7.1.1 Sampling distribution of the odds 74 (3)
ratio
7.1.2 Confidence interval for the odds ratio 77 (1)
7.1.3 Example-coffee drinking and 78 (1)
pancreatic cancer
7.1.4 Small sample adjustments for 79 (2)
estimators of the odds ratio
7.2 The relative risk 81 (2)
7.2.1 Example-coronary heart disease in the 82 (1)
Western Collaborative Group Study
7.3 The excess risk 83 (1)
7.4 The attributable risk 84 (1)
7.5 Comments and further reading 85 (5)
7.5.1 Measurement error or misclassification 86 (4)
7.6 Problems 90 (3)
8 Causal Inference and Extraneous Factors: 93 (30)
Confounding and Interaction
8.1 Causal inference 94 (8)
8.1.1 Counterfactuals 94 (5)
8.1.2 Confounding variables 99 (1)
8.1.3 Control of confounding by 100(2)
stratification
8.2 Causal graphs 102(7)
8.2.1 Assumptions in causal graphs 105(1)
8.2.2 Causal graph associating childhood 106(1)
vaccination to subsequent health condition
8.2.3 Using causal graphs to infer the 107(2)
presence of confounding
8.3 Controlling confounding in causal graphs 109(3)
8.3.1 Danger: controlling for colliders 109(2)
8.3.2 Simple rules for using a causal graph 111(1)
to choose the crucial confounders
8.4 Collapsibility over strata 112(4)
8.5 Comments and further reading 116(3)
8.6 Problems 119(4)
9 Control of Extraneous Factors 123(24)
9.1 Summary test of association in a series 123(5)
of 2 x 2 tables
9.1.1 The Cochran-Mantel-Haenszel test 125(3)
9.1.2 Sample size issues and a historical 128(1)
note
9.2 Summary estimates and confidence 128(6)
intervals for the odds ratio, adjusting for
confounding factors
9.2.1 Woolfs method on the logarithm scale 129(1)
9.2.2 The Mantel-Haenszel method 130(1)
9.2.3 Example-the Western Collaborative 131(2)
Group Study: part 2
9.2.4 Example-coffee drinking and 133(1)
pancreatic cancer: part 2
9.3 Summary estimates and confidence 134(2)
intervals for the relative risk, adjusting
for confounding factors
9.3.1 Example-the Western Collaborative 135(1)
Group Study: part 3
9.4 Summary estimates and confidence 136(2)
intervals for the excess risk, adjusting for
confounding factors
9.4.1 Example-the Western Collaborative 137(1)
Group Study: part 4
9.5 Further discussion of confounding 138(5)
9.5.1 How do adjustments for confounding 138(4)
affect precision?
9.5.2 An empirical approach to confounding 142(1)
9.6 Comments and further reading 143(1)
9.7 Problems 144(3)
10 Interaction 147(18)
10.1 Multiplicative and additive interaction 148(2)
10.1.1 Multiplicative interaction 148(1)
10.1.2 Additive interaction 149(1)
10.2 Interaction and counterfactuals 150(2)
10.3 Test of consistency of association 152(8)
across strata
10.3.1 The Woolf method 153(2)
10.3.2 Alternative tests of homogeneity 155(1)
10.3.3 Example-the Western Collaborative 156(2)
Group Study: part 5
10.3.4 The power of the test for homogeneity 158(2)
10.4 Example of extreme interaction 160(1)
10.5 Comments and further reading 161(1)
10.6 Problems 162(3)
11 Exposures at Several Discrete Levels 165(14)
11.1 Overall test of association 165(2)
11.2 Example-coffee drinking and pancreatic 167(1)
cancer: part 3
11.3 A test for trend in risk 167(4)
11.3.1 Qualitatively ordered exposure 169(1)
variables
11.3.2 Goodness of fit and nonlinear trends 170(1)
in risk
11.4 Example-the Western Collaborative Group 171(2)
Study: part 6
11.5 Example-coffee drinking and pancreatic 173(2)
cancer: part 4
11.6 Adjustment for confounding, exact tests, 175(1)
and interaction
11.7 Comments and further reading 176(1)
11.8 Problems 176(3)
12 Regression Models Relating Exposure to 179(20)
Disease
12.1 Some introductory regression models 181(2)
12.1.1 The linear model 181(2)
12.1.2 Pros and cons of the linear model 183(1)
12.2 The log linear model 183(1)
12.3 The probit model 184(2)
12.4 The simple logistic regression model 186(2)
12.4.1 Interpretation of logistic 187(1)
regression parameters
12.5 Simple examples of the models with a 188(2)
binary exposure
12.6 Multiple logistic regression model 190(6)
12.6.1 The use of indicator variables for 191(5)
discrete exposures
12.7 Comments and further reading 196(1)
12.8 Problems 196(3)
13 Estimation of Logistic Regression Model 199(22)
Parameters
13.1 The likelihood function 199(8)
13.1.1 The likelihood function based on a 201(3)
logistic regression model
13.1.2 Properties of the log likelihood 204(2)
function and the maximum likelihood estimate
13.1.3 Null hypotheses that specify more 206(1)
than one regression coefficient
13.2 Example-the Western Collaborative Group 207(5)
Study: part 7
13.3 Logistic regression with case-control 212(3)
data
13.4 Example-coffee drinking and pancreatic 215(3)
cancer: part 5
13.5 Comments and further reading 218(1)
13.6 Problems 219(2)
14 Confounding and Interaction within Logistic 221(22)
Regression Models
14.1 Assessment of confounding using logistic 221(4)
regression models
14.1.1 Example-the Western Collaborative 223(2)
Group Study: part 8
14.2 Introducing interaction into the 225(2)
multiple logistic regression model
14.3 Example-coffee drinking and pancreatic 227(3)
cancer: part 6
14.4 Example-the Western Collaborative Group 230(1)
Study: part 9
14.5 Collinearity and centering variables 230(5)
14.5.1 Centering independent variables 233(1)
14.5.2 Fitting quadratic models 233(2)
14.6 Restrictions on effective use of maximum 235(1)
likelihood techniques
14.7 Comments and further reading 236(4)
14.7.1 Measurement error 237(1)
14.7.2 Missing data 237(3)
14.8 Problems 240(3)
15 Goodness of Fit Tests for Logistic 243(14)
Regression Models and Model Building
15.1 Choosing the scale of an exposure 243(3)
variable
15.1.1 Using ordered categories to select 244(1)
exposure scale
15.1.2 Alternative strategies 245(1)
15.2 Model building 246(4)
15.3 Goodness of fit 250(4)
15.3.1 The Hosmer-Lemeshow test 252(2)
15.4 Comments and further reading 254(1)
15.5 Problems 255(2)
16 Matched Studies 257(28)
16.1 Frequency matching 257(1)
16.2 Pair matching 258(6)
16.2.1 Mantel-Haenszel techniques applied 262(2)
to pair-matched data
16.2.2 Small sample adjustment for odds 264(1)
ratio estimator
16.3 Example-pregnancy and spontaneous 264(1)
abortion in relation to coronary heart
disease in women
16.4 Confounding and interaction effects 265(4)
16.4.1 Assessing interaction effects of 265(1)
matching variables
16.4.2 Possible confounding and interactive 266(3)
effects due to nonmatching variables
16.5 The logistic regression model for 269(5)
matched data
16.5.1 Example-pregnancy and spontaneous 271(3)
abortion in relation to coronary heart
disease in women: part 2
16.6 Example-the effect of birth order on 274(2)
respiratory distress syndrome in twins
16.7 Comments and further reading 276(3)
16.7.1 When can we break the match? 277(1)
16.7.2 Final thoughts on matching 278(1)
16.8 Problems 279(6)
17 Alternatives and Extensions to the Logistic 285(16)
Regression Model
17.1 Flexible regression model 285(4)
17.2 Beyond binary outcomes and independent 289(1)
observations
17.3 Introducing general risk factors into 290(3)
formulation of the relative hazard-the Cox
model
17.4 Fitting the Cox regression model 293(2)
17.5 When does time at risk confound an 295(2)
exposure-disease relationship?
17.5.1 Time-dependent exposures 296(1)
17.5.2 Differential loss to follow-up 296(1)
17.6 Comments and further reading 297(1)
17.7 Problems 298(3)
18 Epilogue: The Examples 301(2)
References 303(8)
Glossary of Common Terms and Abbreviations 311(8)
Index 319