Essential Statistical Methods for Medical Statistics

by Miller

• ISBN13: 9780444537379
• ISBN10: 0444537376
• Format: Hardcover
• Copyright: 2011-01-10
• Publisher: Elsevier Science

Summary

Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009).While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas.· Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

Table of Contents

Contributors	p. ix
Statistical Methods and Challenges in Epidemiology and Biomedical Research	p. 1
Introduction	p. 1
Characterizing the study cohort	p. 3
Observational study methods and challenges	p. 6
Randomized controlled trials	p. 12
Intermediate, surrogate, and auxiliary outcomes	p. 17
Multiple testing issues and high-dimensional biomarkers	p. 18
Further discussion and the Women's Health Initiative example	p. 20
References	p. 22
Statistical Methods for Assessing Biomarkers and Analyzing Biomarker Data	p. 27
Introduction	p. 27
Statistical methods for assessing biomarkers	p. 28
Statistical methods for analyzing biomarker data	p. 44
Concluding remarks	p. 61
References	p. 61
Linear and Non-Linear Regression Methods in Epidemiology and Biostatistics	p. 66
Introduction	p. 66
Linear models	p. 69
Non-linear models	p. 84
Special topics	p. 93
References	p. 99
Count Response Regression Models	p. 104
Introduction	p. 104
The Poisson regression model	p. 106
Heterogeneity and overdispersion	p. 118
Important extensions of the models for counts	p. 123
Software	p. 142
Summary and conclusions	p. 144
References	p. 144
Mixed Models	p. 146
Introduction	p. 146
Estimation for the linear mixed model	p. 152
Inference for the mixed model	p. 154
Selecting the best mixed model	p. 157
Diagnostics for the mixed model	p. 161
Outliers	p. 163
Missing data	p. 163
Power and sample size	p. 164
Generalized linear mixed models	p. 165
Nonlinear mixed models	p. 167
Mixed models for survival data	p. 168
Software	p. 168
Conclusions	p. 169
References	p. 170
Factor Analysis and Related Methods	p. 174
Introduction	p. 174
Exploratory factor analysis (EFA)	p. 175
Principle components analysis (PCA)	p. 182
Confirmatory factor analysis (CFA)	p. 182
FA with non-normal continuous variables	p. 186
FA with categorical variables	p. 187
Sample size in FA	p. 189
Examples of EFA and CFA	p. 190
Additional resources	p. 196
Appendix A	p. 198
Appendix B	p. 198
References	p. 198
Structural Equation Modeling	p. 202
Models and identification	p. 202
Estimation and evaluation	p. 206
Extensions of SEM	p. 217
Some practical issues	p. 221
References	p. 224
Statistical Modeling in Biomedical Research: Longitudinal Data Analysis	p. 235
Introduction	p. 235
Analysis of longitudinal data	p. 237
Design issues of a longitudinal study	p. 261
References	p. 266
Sequential and Group Sequential Designs in Clinical Trials: Guidelines for Practitioners	p. 269
Introduction	p. 270
Historical background of sequential procedures	p. 271
Group sequential procedures for randomized trials	p. 272
Steps for GSD design and analysis	p. 284
Discussion	p. 285
References	p. 287
Estimation of Marginal Regression Models with Multiple Source Predictors	p. 291
Introduction	p. 291
Review of the generalized estimating equations approach	p. 293
Maximum likelihood estimation	p. 296
Simulations	p. 298
Efficiency calculations	p. 301
Illustration	p. 302
Conclusion	p. 304
References	p. 306
The Bayesian Approach to Experimental Data Analysis	p. 308
Preamble: and if you were a Bayesian without knowing it?	p. 308
Introduction	p. 309
Frequentist and Bayesian inference	p. 311
An illustrative example	p. 316
Other examples of inferences about proportions	p. 328
Concluding remarks and some further topics	p. 335
References	p. 341
Subject Index	p. 345
Table of Contents provided by Ingram. All Rights Reserved.