Statistical Models in Epidemiology

✍ Scribed by David Clayton, Michael Hills

• Publisher: Oxford University Press, USA
• Year: 2013
• Tongue: English
• Leaves: 376
• Edition: 1
• Category: Library

✦ Synopsis

The most important concept in statistics is the probability model. Only by fully understanding this model can one fully understand statistical analysis. Utilizing models in epidemiology, the authors of this self-contained account have chosen to emphasize the role of likelihood. This approach to statistics is both simple and intuitively satisfying. More complex problems can be tackled by natural extensions of the simple methods. This exploration of the statistical basis of epidemiology has been written specifically for professionals and graduate students in epidemiology, clinical epidemiology, or biostatistics. The simple prerequisite--basic training in biology--assumes no previous knowledge and the mathematics is deliberately kept at a manageable level. Based on a highly successful course by two internationally known authors, this book explains the essentials of statistics for all epidemiologists.

✦ Table of Contents

Contents
I PROBABILITY MODELS AND LIKELIHOOD
1 Probability models 3
2 Conditional probability models 10
3 Likelihood 18
4 Consecutive follow-up intervals 27
5 Rates 40
6 Time 53
7 Competing risks and selection 63
8 The Gaussian probability model 71
9 Approximate likelihoods 78
10 Likelihood, probability, and confidence 89
11 Null hypotheses and p-values 96
12 Small studies 110
13 Likelihoods for the rate ratio 122
14 Confounding and standardization 133
15 Comparison of rates within strata 141
16 Case-control studies 153
17 Likelihoods for the odds ratio 166
18 Comparison of odds within strata 175
19 Individually matched case-control studies 186
20 Tests for trend 197
21 The size of investigations 205
22 Introduction to regression models 217
23 Poisson and logistic regression 227
24 Testing hypotheses 237
25 Models for dose-response 249
26 More about interaction 261
27 Choice and interpretation of models 271
28 Additivity and synergism 282
29 Conditional logistic regression 290
30 Cox's regression analysis 298
31 Time-vary ing explanatory variables 307
32 Three examples 319
33 Nested case-control studies 329
34 Gaussian regression models 336
35 Postscript 346
III APPENDICES
A Exponentials and logarithms 351
B Some basic calculus 354
C Approximate profile likelihoods 357
D Table of the chi-squared distribution 363
Index 365