UID:
almafu_9959242967302883
Format:
1 online resource (1119 p.)
Edition:
1st ed.
ISBN:
1-281-87175-3
,
9786611871758
,
981-238-875-3
Content:
This book presents new and powerful advanced statistical methods that have been used in modern medicine, drug development, and epidemiology. Some of these methods were initially developed for tackling medical problems.All 29 chapters are self-contained. Each chapter represents the new development and future research topics for a medical or statistical branch. For the benefit of readers with different statistical background, each chapter follows a similar style: the explanation of medical challenges, statistical ideas and strategies, statistical methods and techniques, mathematical remarks and
Note:
Description based upon print version of record.
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PREFACE; ABOUT THE EDITORS; Contents; Section 1 Statistical Methods in Biomedical Research; 1 HISTORY OF STATISTICAL THINKING IN MEDICINE; 1. Introduction; 2. Laplace and His Vision; 3. Louis and Numerical Method; 4. Statistical Analysis Versus Laboratory Investigation; 5. The Beginning of Modern Statistics; 6. The Beginning of Medical Statistics; 7. Randomization in Experimentation; 8. First Randomized Controlled Clinical Trial; 9. Government Regulation and Statistics; 10. Epilogue; Acknowledgment; References; 2 EVALUATION OF DIAGNOSTIC TEST'S ACCURACY IN THE PRESENCE OF VERIFICATION BIAS
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1. Introduction2. A Single Binary Test; 2.1. An overview; 2.2. Estimation of a single test; 2.3. An hepatic scintigraph example; 3. Comparison of Two Correlated Binary Tests; 3.1. An overview; 3.2. The ML approach; 4. A Single Ordinal-Scale Test; 4.1. An overview; 4.2. Estimation of a single ROC curve without covariates; 4.3. Estimation of ROC curves with covariates; 4.4. Estimation of the area under an ROC curve; 4.5. A real example with fever of uncertain origin; 5. Comparison of Two Correlated Ordinal-Scale Tests; 5.1. An overview; 5.2. A weighted GEE approach for ROC curves
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5.3. A likelihood-based approach for ROC areas5.4. Availability of computer software; 6. Discussion; References; 3 STATISTICAL METHODS FOR DEPENDENT DATA; 1. Introduction; 2. Examples of Dependent Data; 2.1. Example 1. Randomized block design; 2.2. Example 2. Cluster sampling; 2.3. Example 3. Toxicological study; 2.4. Example 4. Crossover design; 2.5. Example 5. Repeated measurement, linear regression; 2.6. Example 6. Pharmacokinetics study, repeated measurements, nonlinear regression; 2.7. Example 7. Mmulti-center clinical study, ranked data; 2.8. Example 8. Repeated measurement, count data
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2.9. Other examples3. Common Structures of Intra-unit Correlation for Dependent Data; 3.1. A simple case; 3.2. Complicated cases; 4. ANOVA Methods and Its Limitation; 4.1. Parameter estimations for dependent data; 4.2. ANOVA with random effect; 4.3. Example 9. Analysis of randomized block design data; 4.4. Example 10. 4 X 4 cross-over design; 4.5. The condition of using ANOVA; 5. GEE for Dependent Data; 5.1. Introduction of GEE; 5.2. Parameters estimations of GEE; 5.3. Analysis of examples; 6. Multilevel Models for Dependent Data; 6.1. Introduction of multilevel model
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6.2. Estimation of parameters of multilevel model6.3. Example 14. Analysis of data in Example 1; 6.4. Example 15. Analysis of data in Example 7; 6.5. Multilevel logistic regression; 6.6. Multilevel Probit model and complementary log-log model; 6.7. Example 16; 6.8. Multilevel Poisson regression model; 6.9. Example 17. Fitting a 2-level Poisson regression model for data in Example 8; 6.10. Multilevel logistic models for multiple response categories; 6.11. Example 18. Analysis of data in Example 7; 6.12. Relationship between intra-unit correlation and explanatory variable
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6.13. Example 19. Analysis of the data in Example 5
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English
Additional Edition:
ISBN 981-02-4800-8
Additional Edition:
ISBN 981-02-4799-0
Language:
English
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