# Statistical Analysis of Epidemiologic Data

Oxford University Press, May 13, 2004 - Medical - 512 pages
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.