This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the
authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of
the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.
This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the
authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of
the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.
I. Probability Models and Likelihood
1: Probability models
2: Conditional probability models
3: Likelihood
4: Consecutive follow-up intervals
5: Rates
6: Time
7: Competing risks and selection
8: The Gaussian probability model
9: Approximate likelihoods
10: Likelihood, probability, and confidence
11: Null hypotheses and p-values
12: Small studies
13: Likelihoods for the rate ratio
14: Confounding and standardization
15: Comparison of rates within strata
16: Case-control studies
17: Likelihoods for the odds ratio
18: Comparison of odds within strata
19: Individually matched case-control studies
20: Tests for trend
21: The size of investigations
II. Regression Models
22: Introduction to regression models
23: Poission and logistic regression
24: Testing hypotheses
25: Models for dose-response
26: More about interaction
27: Choice and interpretation of models
28: Additivity and synergism
29: Conditional logistic regression
30: Cox's regression analysis
31: Time-varying explanatory variables
32: Three examples
33: Nested case-control studies
34: Gaussian regression models
35: Postscript
III. Appendices
A. Exponentials
B. Some basic calculus
C. Approximate profile likelihoods
D. Table of the Chi-squared distribution
Index
David Clayton, Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research Michael Hills, London School of Hygiene and Tropical Medicine
`Unlike many textbooks in epidemiology, there is no long wordy
preamble. The characteristic style is set straight away. The book
is also highly successful in presenting a unified approach. What is
also striking, is that the authors have managed to say something
useful and clear about many of the all too numerous minor problems
that are inevitably encountered in practice. In my view this is
simply an excellent text.
'
Andrew Pickles, Institute of Psychiatry, London, Statistical
Methods in Medical Research 1994:3
`An excellent text which provides the simplest and most logical
exposition that I have seen of the statistical foundations for
current techniques for analysing epidemiological data, and provides
an excellent preparation for more detailed treatments.
'
Australasian Epidemiological Association News, 12/94
`Provides probably the most coherent and logical exposition of the
use of statistical models in epidemiology that is currently
available ... an excellent text which provides the simplest and
most logical exposition that I have seen of the statistical
foundations for current techniques for analysing epidemiological
data, and provides an excellent preparation for more detailed
treatments.
'
AEA News 12/94
`Clayton and Hills have filled the gap with an interesting text
which is based mainly on probability models and likelihood. This is
an unusual approach. but is precisely what is missing in many other
textbooks for epidemiologists ... this is an important text for
those interested in understanding statistical reasoning in
epidemiology.
'
Maria Blettner, International Journal of Epidemiology
`The authors have produced a text that will be extremely valuable
to those teaching epidemiologic methods... Statistical Models in
Epidemiology courageously cuts new paths into the traditional
epidemiologic approach to statistical training.
'
Journal of the American Statistics Association
`This book gives some very clear explanations ... Each point is
well illustrated with small examples and there are exercises
throughout. It is pleasing to see full solution to all the
exercises.
'
Public Health (1994) 108
|
Ask a Question About this Product More... |
|
