Statistical Analysis of Epidemiologic Data

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Oxford University Press, May 13, 2004 - Medical - 512 pages
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Analytic procedures suitable for the study of human disease are scattered throughout the statistical and epidemiologic literature. Explanations of their properties are frequently presented in mathematical and theoretical language. This well-established text gives readers a clear understanding of the statistical methods that are widely used in epidemiologic research without depending on advanced mathematical or statistical theory. By applying these methods to actual data, Selvin reveals the strengths and weaknesses of each analytic approach. He combines techniques from the fields of statistics, biostatistics, demography and epidemiology to present a comprehensive overview that does not require computational details of the statistical techniques described. For the Third Edition, Selvin took out some old material (e.g. the section on rarely used cross-over designs) and added new material (e.g. sections on frequently used contingency table analysis). Throughout the text he enriched existing discussions with new elements, including the analysis of multi-level categorical data and simple, intuitive arguments that exponential survival times cause the hazard function to be constant. He added a dozen new applied examples to illustrate such topics as the pitfalls of proportional mortality data, the analysis of matched pair categorical data, and the age-adjustment of mortality rates based on statistical models. The most important new feature is a chapter on Poisson regression analysis. This essential statistical tool permits the multivariable analysis of rates, probabilities and counts.
 

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Contents

Measures of Risk Rates and Probabilities
1
Rates
2
Probabilities
6
Incidence and prevalence
8
Survival probabilities and hazard rates
12
Statistical properties of probabilities calculated from mortality or disease data
14
smoothing transforming and adjusting
22
Variation and Bias
40
general considerations
248
Centering
249
The WCGS additive logistic model
252
Casecontrol sampling
259
The Analysis of Count Data Poisson Model
263
Simplest Poisson model
264
technical description
265
Illustration of the Poisson model
266

A simple model
41
The 𝐭test
43
Selection Bias
48
Confounder bias
50
Ecologic bias
52
Comparison of k groups
54
Interaction contrasts
58
Twoway analysis
62
Misclassification bias
69
Statistical Power and Sample Size Calculations
75
sample mean
79
relative risk
80
onesample test of a proportion
82
twosample test of proportions
84
Loss of statistical power and bias from grouping continuous data
88
Cohort Data Description and Illustration
93
model
94
Birth cohort effect and proportional mortality data
97
Median polish analysis
100
Mean polish analysis
102
Four examples
104
prostatic cancer data
110
Spatial Data Analysis and Estimation
120
Poisson model
121
Nearestneighbor analysis
125
Transformed maps
130
Spatial distribution about a point
135
Timedistance spatial analysis
138
Randomization test
143
randomization test
146
Bootstrap estimation and analysis
149
The 2 X 𝘬 table and the 2 X 2 X 2 Table
159
Independence and homogeneity
160
Regression
165
comparison of two means
169
Ridit probability analysis
173
The 2 X 2 X 2 contingency table
179
The Analysis of Contingency Table Data Logistic Model I
190
discrete case
191
The 2 X 2 X 2 table
199
The 2 X 𝘬 table
208
The 2 X 2 X 𝘬 table
213
The multiway table
221
discrete case
224
Summarizing a series of 2 X 2 tables
227
The Analysis of Binary Data Logistic Model II
236
Bivariate logistic regression
240
Hodgkins disease
268
CHD risk by smoking behavior type
273
application of the Poisson model
276
a twoway classification
278
a threeway classification
283
The Analysis of Matched Data Three Approaches
291
Frequency matching
292
Poststratification
294
continuous variable
296
binary risk factor
300
Confidence interval for the odds ratio
305
Evaluating the estimated odds ratio
307
Disregarding matching
309
Interactions with the matching variable
311
Matched sets using more than one control
313
multilevel categorical risk factor
317
Conditional logistic analysis
320
Life Table Analysis An Introduction
335
construction
336
Life table survival function
350
three applications of life table techniques
356
Competing risks
372
Survival Data Estimation of Risk
378
Parametric model
379
Age adjustment of rates
382
Censored and truncated data
384
parametric estimate
386
nonparametric estimate
390
Mean survival time from censored data
393
Goodnessoffit
396
Twosample data
399
The Wilcoxon test and the Gehan generalization to survival data
407
Survival Data Proportional Hazards Model
412
Simplest case
413
The proportional hazards model
417
Plotting survival curves
420
Four applications of a proportional hazards model
421
Dependency of followup time
439
Appendix
443
Binomial and Poisson probability distributions
445
The odds ratio and its properties
448
Partitioning the chisquare statistic
454
Maximum likelihood estimation and likelihood functions
457
Problems
464
References
479
Index
487
Copyright

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Page 480 - Statistical Methods for Rates and Proportions, John Wiley and Sons, New York, 1973.

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