Design and Analysis of Experiments in the Health Sciences
1. Auflage August 2012
256 Seiten, Hardcover
Lehrbuch
Kurzbeschreibung
This book is a primer on the design and analysis of experiments, and the first question to be addressed is: Why do an experiment? The author begins with definitions of an experiment and discusses three major types of experiments: intrinsic, comparative, and bioequivalent. As with any science, there are a number of basic concepts that need to be defined and discussed, and the basic terminology used throughout the book is defined early on. Six chapters are dedicated to six types of designs and simple extensions that form the basis for most experimental structures. Each of the designs in these six chapters is discussed under the same eight headings: Description; Randomization; Hypothesis and Sample Size; Estimation and Analysis; Example; Notes; and Problems. The title, Design Rules!, has a deliberate ambiguity to it. The first meaning implies that the essential standard of a good experiment is its design. If the design is sound, it is difficult to ruin the analysis and the interpretation. It is much easier to salvage a bad analysis of a good experiment than a good analysis of a bad experiment. The second meaning takes the liberty of using a noun as a verb: rules for guiding to good experimentation.
Weitere Versionen
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.
1. The Basics 1
1.1 Four Basic Questions 1
1.2 Variation 5
1.3 Principles of Design and Analysis 6
1.4 Experiments and observational studies 11
1.5 Illustrative applications of principles 14
1.6 Experiments in the health sciences 15
1.7 Adaptive Allocation 19
1.8 Sample Size Calculations 23
1.9 Statistical models for the data 25
1.10 Analysis and presentation 27
1.11 Notes 31
1.12 Summary 33
1.13 Problems 34
2. Completely Randomized Experiments 41
2.1 Randomization 42
2.2 Hypotheses and Sample Size 42
2.3 Estimation and Analysis 43
2.4 Example 44
2.5 Discussion and Extensions 46
2.6 Randomization 54
2.7 Hypotheses and Sample Size 54
2.8 Estimation and Analysis 54
2.9 Example 55
2.10 Discussion and Extensions 59
2.11 Notes 61
2.12 Summary 70
2.13 Problems 70
3. Randomized Block Designs 83
3.1 Randomization 84
3.2 Hypotheses and Sample Size 84
3.3 Estimation and Analysis 85
3.4 Example 85
3.5 Discussion and Extensions 89
3.6 Randomization 99
3.7 Hypotheses and Sample Size 99
3.8 Estimation and Analysis 99
3.9 Example 100
3.10 Discussion and Extensions 102
3.11 Randomization 104
3.12 Hypotheses and Sample Size 104
3.13 Estimation and Analysis 106
3.14 Example 106
3.15 Discussion and Extensions 110
3.16 Notes 111
3.17 Summary 113
3.18 Problems 114
4. Factorial Designs 121
4.1 Randomization 123
4.2 Hypotheses and Sample Size 124
4.3 Estimation and Analysis 125
4.4 Example 1 126
4.5 Example 2 130
4.6 Notes 132
4.7 Summary 142
4.8 Problems 142
5. Multilevel Experiments 149
5.1 Randomization 151
5.2 Hypotheses and Sample Size 151
5.3 Estimation and Analysis 152
5.4 Example 154
5.5 Discussion and Extensions 162
5.6 Notes 164
5.7 Summary 165
5.8 Problems 166
6. Repeated Measures Designs 171
6.1 Randomization 172
6.2 Hypotheses and Sample Size 173
6.3 Estimation and Analysis 173
6.4 Example 176
6.5 Discussion and Extensions 179
6.6 Notes 182
6.7 Summary 184
6.8 Problems 184
7. Randomized Clinical Trials 191
7.1 Endpoints 194
7.2 Randomization 196
7.3 Hypotheses and Sample Size 197
7.4 Follow-up 198
7.5 Estimation and Analysis 198
7.6 Examples 200
7.7 Discussion and Extensions 205
7.8 Notes 214
7.9 Resources 220
7.10 Summary 220
7.11 Problems 221
8. Microarrays 231
8.1 Introduction 231
8.2 Genes, Gene Expression and Microarrays 231
8.3 Examples of microarray studies 240
8.4 Replication and sample size 242
8.5 Blocking and microarrays 244
8.6 Randomization and microarrays 246
8.7 Microarray data analysis issues 247
8.8 Data analysis example 259
8.9 Notes 262
8.10 Summary 263
8.11 Problems 263
Bibliography 207
Author Index 217
Subject Index 223
"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)
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.
