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El. knyga: Design and Analysis of Experiments in the Health Sciences

(Universityof Washington), (Universityof Washington)
  • Formatas: PDF+DRM
  • Išleidimo metai: 07-Jun-2012
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118279694
Kitos knygos pagal šią temą:
Design and Analysis of Experiments in the Health SciencesDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 07-Jun-2012
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118279694
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"This volume provides technical professionals and students with three uniquely integrative enhancements to the study of predictive modeling not typically found in data-mining books: an applied approach, immediate practice using Microsoft Excel, and easy-to-use access to multiple online model-building tools. Since actual datasets are employed, users deal with real-life modeling issues and situations such as handling missing values, applying variable transformations, and addressing outliers, among others. An easy-to-learn Microsoft Excel add-in (Predictive MinerXL) and all applicable datasets are available for free on an associated Web site"--



An accessible and practical approach to the design and analysis of experiments in the health sciences

Design and Analysis of Experiments in the Health Sciences provides a balanced presentation of design and analysis issues relating to data in the health sciences and emphasizes new research areas, the crucial topic of clinical trials, and state-of-the- art applications.

Advancing the idea that design drives analysis and analysis reveals the design, the book clearly explains how to apply design and analysis principles in animal, human, and laboratory experiments while illustrating topics with applications and examples from randomized clinical trials and the modern topic of microarrays. The authors outline the following five types of designs that form the basis of most experimental structures:

  • Completely randomized designs
  • Randomized block designs
  • Factorial designs
  • Multilevel experiments
  • Repeated measures designs

A related website features a wealth of data sets that are used throughout the book, allowing readers to work hands-on with the material. In addition, an extensive bibliography outlines additional resources for further study of the presented topics.

Requiring only a basic background in statistics, Design and Analysis of Experiments in the Health Sciences is an excellent book for introductory courses on experimental design and analysis at the graduate level. The book also serves as a valuable resource for researchers in medicine, dentistry, nursing, epidemiology, statistical genetics, and public health.

Recenzijos

Overall, Design and Analysis of Experiments in the Health Sciencesis a balanced and approachable text suitable for a graduate level experimental design course, and will prove particularly useful to practitioners in the health sciences.  (Journal of Biopharmaceutical Statistics, 1 January 2013)

The book will be a valuable resource for researchers in medicine, dentistry, and the public health sciences.  The authors are faculty members in the Department of Biostatistics at the University of Washington in Seattle.  (Journal of Clinical Research Best Practices, 1 September 2012)

 

Preface xiii
1 The Basics
1(30)
1.1 Four Basic Questions
1(3)
1.2 Variation
4(1)
1.3 Principles of Design and Analysis
5(4)
1.4 Experiments and Observational Studies
9(2)
1.5 Illustrative Applications of Principles
11(1)
1.6 Experiments in the Health Sciences
12(3)
1.7 Adaptive Allocation
15(3)
1.7.1 Equidistribution
15(1)
1.7.2 Adaptive Allocation Techniques
16(2)
1.8 Sample Size Calculations
18(2)
1.9 Statistical Models for the Data
20(2)
1.10 Analysis and Presentation
22(2)
1.10.1 Graph the Data in Several Ways
22(1)
1.10.2 Assess Assumptions of the Statistical Model
22(1)
1.10.3 Confirmatory and Exploratory Analysis
23(1)
1.10.4 Missing Data Need Careful Accounting
23(1)
1.10.5 Statistical Software
24(1)
1.11 Notes
24(2)
1.11.1 Characterization Studies
24(1)
1.11.2 Additional Comments on Balance
25(1)
1.11.3 Linear and Nonlinear Models
25(1)
1.11.4 Analysis of Variance Versus Regression Analysis
26(1)
1.12 Summary
26(1)
1.13 Problems
26(5)
2 Completely Randomized Designs
31(32)
2.1 Randomization
31(1)
2.2 Hypotheses and Sample Size
32(1)
2.3 Estimation and Analysis
32(2)
2.4 Example
34(2)
2.5 Discussion and Extensions
36(5)
2.5.1 Preparing Data for Computer Analysis
36(1)
2.5.2 Treatment Assignment in this Example
37(1)
2.5.3 Check on Randomization
37(1)
2.5.4 Partitioning the Treatment Sum of Squares
37(1)
2.5.5 Alternative Endpoints
38(1)
2.5.6 Dummy Variables
38(1)
2.5.7 Contrasts
39(2)
2.6 Randomization
41(1)
2.7 Hypotheses and Sample Size
41(1)
2.8 Estimation and Analysis
41(1)
2.9 Example
42(2)
2.10 Discussion and Extensions
44(3)
2.10.1 Two Roles for ANCOVA
44(1)
2.10.2 Partitioning of Sums of Squares
45(1)
2.10.3 Assumption of Parallelism
46(1)
2.11 Notes
47(6)
2.11.1 Constrained Randomization
47(1)
2.11.2 Assumptions of the Analysis of Variance and Covariance
48(1)
2.11.3 When the Assumptions Don't Hold
49(1)
2.11.4 Alternative Graphical Displays
50(1)
2.11.5 Sample Sizes for More Than Two Levels
51(1)
2.11.6 Limitations of Computer Output
51(1)
2.11.7 Unequal Sample Sizes
51(1)
2.11.8 Design Implications of the CRD
51(1)
2.11.9 Power and Alternative Hypotheses
52(1)
2.11.10 Regression or Analysis of Variance?
52(1)
2.11.11 Bioassay
52(1)
2.12 Summary
53(1)
2.13 Problems
53(10)
3 Randomized Block Designs
63(30)
3.1 Randomization
64(1)
3.2 Hypotheses and Sample Size
64(1)
3.3 Estimation and Analysis
64(1)
3.4 Example
65(2)
3.5 Discussion and Extensions
67(10)
3.5.1 Evaluating Model Assumptions
67(2)
3.5.2 Multiple Comparisons
69(2)
3.5.3 Number of Treatments and Block Size
71(1)
3.5.4 Missing Data
71(1)
3.5.5 Does It Always Pay to Block?
71(1)
3.5.6 Concomitant Variables
72(2)
3.5.7 Imbalance
74(3)
3.6 Randomization
77(1)
3.7 Hypotheses and Sample Size
77(1)
3.8 Estimation and Analysis
77(1)
3.9 Example
77(2)
3.10 Discussion and Extensions
79(1)
3.10.1 Implications of the Model
79(1)
3.10.2 Number of Latin Squares
79(1)
3.11 Randomization
80(1)
3.12 Hypotheses and Sample Size
81(1)
3.13 Estimation and Analysis
82(1)
3.14 Example
82(3)
3.15 Discussion and Extensions
85(1)
3.15.1 Partially Balanced Incomplete Block Designs
85(1)
3.16 Notes
86(2)
3.16.1 Analysis Follows Design
86(1)
3.16.2 Relative Efficiency
86(1)
3.16.3 Additivity of the Model
87(1)
3.17 Summary
88(1)
3.18 Problems
88(5)
4 Factorial Designs
93(24)
4.1 Randomization
95(1)
4.2 Hypotheses and Sample Size
95(1)
4.3 Estimation and Analysis
96(1)
4.4 Example 1
97(3)
4.5 Example 2
100(3)
4.6 Notes
103(6)
4.6.1 Regression Analysis Approaches
103(2)
4.6.2 Almost Factorial
105(1)
4.6.3 Design Structure and Factor Structure
105(1)
4.6.4 Effect and Interaction Tables
105(1)
4.6.5 Balanced Design
105(1)
4.6.6 Missing Data
106(1)
4.6.7 Fixed, Random, and Mixed Effects Models
106(2)
4.6.8 Fractional Factorials
108(1)
4.7 Summary
109(1)
4.8 Problems
110(7)
5 Multilevel Designs
117(18)
5.1 Randomization
118(1)
5.2 Hypotheses and Sample Size
118(1)
5.3 Estimation and Analysis
119(2)
5.4 Example
121(6)
5.5 Discussion and Extensions
127(2)
5.5.1 Whole-Plot and Split-Plot Variability
127(1)
5.5.2 Getting the Computer to Do the Right Analysis
128(1)
5.6 Notes
129(1)
5.6.1 Fractional Factorials---Example
129(1)
5.6.2 Missing Data
129(1)
5.7 Summary
130(1)
5.8 Problems
130(5)
6 Repeated Measures Designs
135(14)
6.1 Randomization
136(1)
6.2 Hypotheses and Sample Size
136(1)
6.3 Estimation and Analysis
137(2)
6.4 Example
139(3)
6.5 Discussion and Extensions
142(1)
6.6 Notes
143(1)
6.6.1 RBD and RMD
143(1)
6.6.2 Missing Data: The Fundamental Challenge in RMD
143(1)
6.6.3 Correlation Structure
144(1)
6.6.4 Derived Variable Analysis
144(1)
6.7 Summary
144(1)
6.8 Problems
145(4)
7 Randomized Clinical Trials
149(30)
7.1 Endpoints
151(1)
7.2 Randomization
152(1)
7.3 Hypotheses and Sample Size
153(1)
7.4 Follow-Up
154(1)
7.5 Estimation and Analysis
154(1)
7.6 Examples
155(4)
7.7 Discussion and Extensions
159(4)
7.7.1 Statistical Significance and Clinical Importance
159(2)
7.7.2 Ethics
161(1)
7.7.3 Reporting
162(1)
7.8 Notes
163(8)
7.8.1 Multicenter Trials
163(4)
7.8.2 International Harmonization
167(1)
7.8.3 Data Safety Monitoring
167(1)
7.8.4 Ancillary Studies
168(1)
7.8.5 Subgroup Analysis and Data Mining
168(1)
7.8.6 Meta-Analysis
169(1)
7.8.7 Authorship and Recognition
169(1)
7.8.8 Communication
169(1)
7.8.9 Data Sharing
170(1)
7.8.10 N-of-1 Trials
170(1)
7.9 Resources
171(1)
7.10 Summary
171(1)
7.11 Problems
171(8)
8 Microarrays
179(28)
8.1 Introduction
179(1)
8.2 Genes, Gene Expression, and Microarrays
179(7)
8.2.1 Genes and Gene Expression
179(1)
8.2.2 Gene Expression Microarrays
180(6)
8.3 Examples of Microarray Studies
186(2)
8.4 Replication and Sample Size
188(1)
8.5 Blocking and Microarrays
189(1)
8.6 Randomization and Microarrays
190(1)
8.7 Microarray Data Analysis Issues
191(9)
8.7.1 Image Analysis
191(2)
8.7.2 Data Preprocessing
193(3)
8.7.3 Identifying Differentially Expressed Genes
196(1)
8.7.4 Multiple Testing
196(2)
8.7.5 Gene Set Analysis
198(1)
8.7.6 The Class Prediction Problem
198(2)
8.8 Data Analysis Example
200(2)
8.9 Notes
202(1)
8.9.1 Sample Size
202(1)
8.9.2 FDR Estimation
202(1)
8.9.3 Evaluation of Data Preprocessing Methods
203(1)
8.10 Summary
203(1)
8.11 Problems
203(4)
Bibliography 207(10)
Author Index 217(6)
Subject Index 223
GERALD VAN BELLE, PhD, is Professor Emeritus in the Departments of Biostatistics and Environmental and Occupational Health Sciences at the University of Washington. A Fellow of the American Statistical Association and the American Association for the Advancement of Science, he has published more than 140 articles in the areas of experimental design and data characterization as well as analysis with application to neurodegenerative diseases, effects of air pollution on health and toxicology, and clinical trials in resuscitation outcomes research.

KATHLEEN F. KERR, PhD, is Associate Professor of Biostatistics at the University of Washington. A former Burroughs Wellcome postdoctoral fellow in mathematics and molecular biology, Dr. Kerr currently serves as associate editor of PLoS Genetics and Statistical Applications in Genetics and Molecular Biology. Her research interests include gene expression microarrays, statistical genetics, experimental design, and biomarker research.
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