UID:
edoccha_9960072749102883
Format:
1 online resource (363 p.)
ISBN:
1-282-95549-7
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9786612955495
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0-444-53738-4
Content:
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
Note:
Description based upon print version of record.
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Front Cover; Essential Statistical Methods for Medical Statistics; Copyright; Table of Contents; Contributors; Chapter 1: Statistical Methods and Challenges in Epidemiology and Biomedical Research; 1. Introduction; 2. Characterizing the study cohort; 3. Observational study methods and challenges; 4. Randomized controlled trials; 5. Intermediate, surrogate, and auxiliary outcomes; 6. Multiple testing issues and high-dimensional biomarkers; 7. Further discussion and the Women's Health Initiative example; Acknowledgement; References
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Chapter 2: Statistical Methods for Assessing Biomarkers and Analyzing Biomarker Data1. Introduction; 2. Statistical methods for assessing biomarkers; 3. Statistical methods for analyzing biomarker data; 4. Concluding remarks; References; 3. Linear and Non-Linear Regression Methods in Epidemiology and Biostatistics; 1. Introduction; 2. Linear models; 3. Non-linear models; 4. Special topics; References; 4. Count Response Regression Models; 1. Introduction; 2. The Poisson regression model; 3. Heterogeneity and overdispersion; 4. Important extensions of the models for counts; 5. Software
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6. Summary and conclusionsReferences; 5. Mixed Models; 1. Introduction; 2. Estimation for the linear mixed model; 3. Inference for the mixed model; 4. Selecting the best mixed model; 5. Diagnostics for the mixed model; 6. Outliers; 7. Missing data; 8. Power and sample size; 9. Generalized linear mixed models; 10. Nonlinear mixed models; 11. Mixed models for survival data; 12. Software; 13. Conclusions; References; 6. Factor Analysis and Related Methods; 1. Introduction; 2. Exploratory factor analysis (EFA); 3. Principle components analysis (PCA); 4. Confirmatory factor analysis (CFA)
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5. FA with non-normal continuous variables6. FA with categorical variables; 7. Sample size in FA; 8. Examples of EFA and CFA; 9. Additional resources; Appendix A:. PRELIS and LISREL code for the CFA example with continuous MVs; Appendix B:. Mplus code for CFA example with categorical MVs; References; 7. Structural Equation Modeling; 1. Models and identification; 2. Estimation and evaluation; 3. Extensions of SEM; 4. Some practical issues; Acknowledgement; References; 8. Statistical Modeling in Biomedical Research: Longitudinal Data Analysis; 1. Introduction; 2. Analysis of longitudinal data
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3. Design issues of a longitudinal studyAcknowledgements; References; 9. Sequential and Group Sequential Designs in Clinical Trials: Guidelines for Practitioners; 1. Introduction; 2. Historical background of sequential procedures; 3. Group sequential procedures for randomized trials; 4. Steps for GSD design and analysis; 5. Discussion; Acknowledgement; References; 10. Estimation of Marginal Regression Models with Multiple Source Predictors; 1. Introduction; 2. Review of the generalized estimating equations approach; 3. Maximum likelihood estimation; 4. Simulations; 5. Efficiency calculations
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6. Illustration
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English
Additional Edition:
ISBN 0-444-53737-6
Language:
English
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