Statistics for epidemiology:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
Chapman & Hall/CRC
2004
|
| Series: | Texts in statistical science series
[58] |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIV, 333 S. graph. Darst. |
| ISBN: | 1584884339 |
Staff View
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Record in the Search Index
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| adam_text | Contents
1 Introduction 1
1.1 Disease processes 1
1.2 Statistical approaches to epidemiological data 2
1.2.1 Study design 3
1.2.2 Binary outcome data 4
1.3 Causality 5
1.4 Overview 5
1.4.1 Caution: what is not covered 7
1.5 Comments and further reading 7
2 Measures of Disease Occurrence 9
2.1 Prevalence and incidence 9
2.2 Disease rates 12
2.2.1 The hazard function 13
2.3 Comments and further reading 15
2.4 Problems 16
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
3.6 Comments and further reading 28
3.7 Problems 29
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
x CONTENTS
4.8 Comments and further reading 40
4.9 Problems 41
5 Study Designs 43
5.1 Population based studies 45
5.1.1 Example—mother s marital status and infant birth weight . 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
5.5 Comments and further reading 55
5.6 Problems 56
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 Comments and further reading 68
6.4.1 Alternative formulations of the x2 test statistic 69
6.4.2 Whenis the sample size too small to do a/2 test? 70
6.5 Problems 71
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 Comments and further reading 85
7.5.1 Measurement error or misclassification 86
7.6 Problems 90
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
CONTENTS xi
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 Ill
8.4 Collapsibility over strata 112
8.5 Comments and further reading 116
8.6 Problems 119
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
9.6 Comments and further reading 143
9.7 Problems 144
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
xii CONTENTS
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
10.5 Comments and further reading 161
10.6 Problems 162
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
11.7 Comments and further reading 176
11.8 Problems 176
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
12.7 Comments and further reading 196
12.8 Problems 196
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
13.5 Comments and further reading 218
13.6 Problems 219
CONTENTS xiii
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 Comments and further reading 236
14.7.1 Measurement error 237
14.7.2 Missing data 237
14.8 Problems 240
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
15.4 Comments and further reading 254
15.5 Problems 255
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 Comments and further reading 276
xiv CONTENTS
16.7.1 When can we break the match? 277
16.7.2 Final thoughts on matching 278
16.8 Problems 279
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
17.6 Comments and further reading 297
17.7 Problems 298
18 Epilogue: The Examples 301
References 303
Glossary of Common Terms and Abbreviations 311
Index 319
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| spelling | Jewell, Nicholas P. 1952- Verfasser (DE-588)172590639 aut Statistics for epidemiology Nicholas P. Jewell Boca Raton [u.a.] Chapman & Hall/CRC 2004 XIV, 333 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Texts in statistical science series [58] Biometrie gtt Epidemiologia larpcal Epidemiologie gtt Inferência estatística larpcal Statistische methoden gtt Épidémiologie - Méthodes statistiques Epidemiologic Methods Epidemiology Statistical methods Statistics as Topic methods Epidemiologie (DE-588)4015016-1 gnd rswk-swf (DE-588)4056995-0 Statistik gnd-content Epidemiologie (DE-588)4015016-1 s b DE-604 Texts in statistical science series [58] (DE-604)BV022819715 58 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010321727&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Jewell, Nicholas P. 1952- Statistics for epidemiology Texts in statistical science series Biometrie gtt Epidemiologia larpcal Epidemiologie gtt Inferência estatística larpcal Statistische methoden gtt Épidémiologie - Méthodes statistiques Epidemiologic Methods Epidemiology Statistical methods Statistics as Topic methods Epidemiologie (DE-588)4015016-1 gnd |
| subject_GND | (DE-588)4015016-1 (DE-588)4056995-0 |
| title | Statistics for epidemiology |
| title_auth | Statistics for epidemiology |
| title_exact_search | Statistics for epidemiology |
| title_full | Statistics for epidemiology Nicholas P. Jewell |
| title_fullStr | Statistics for epidemiology Nicholas P. Jewell |
| title_full_unstemmed | Statistics for epidemiology Nicholas P. Jewell |
| title_short | Statistics for epidemiology |
| title_sort | statistics for epidemiology |
| topic | Biometrie gtt Epidemiologia larpcal Epidemiologie gtt Inferência estatística larpcal Statistische methoden gtt Épidémiologie - Méthodes statistiques Epidemiologic Methods Epidemiology Statistical methods Statistics as Topic methods Epidemiologie (DE-588)4015016-1 gnd |
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