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.

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.

Preface xiii

1 The Basics 1

1.1 Four Basic Questions 1

1.2 Variation 4

1.3 Principles of Design and Analysis 5

1.4 Experiments and Observational Studies 9

1.5 Illustrative Applications of Principles 11

1.6 Experiments in the Health Sciences 12

1.7 Adaptive Allocation 15

1.7.1 Equidistribution 15

1.7.2 Adaptive Allocation Techniques 16

1.8 Sample Size Calculations 18

1.9 Statistical Models for the Data 20

1.10 Analysis and Presentation 22

1.10.1 Graph the Data in Several Ways 22

1.10.2 Assess Assumptions of the Statistical Model 22

1.10.3 Confirmatory and Exploratory Analysis 23

1.10.4 Missing Data Need Careful Accounting 23

1.10.5 Statistical Software 24

1.11 Notes 24

1.11.1 Characterization Studies 24

1.11.2 Additional Comments on Balance 25

1.11.3 Linear and Nonlinear Models 25

1.11.4 Analysis of Variance versus Regression Analysis 26

1.12 Summary 26

1.13 Problems 26

2 Completely Randomized Designs 31

2.1 Randomization 31

2.2 Hypotheses and Sample Size 32

2.3 Estimation and Analysis 32

2.4 Example 34

2.5 Discussion and Extensions 36

2.5.1 Preparing Data for Computer Analysis 36

2.5.2 Treatment Assignment in this Example 37

2.5.3 Check on Randomization 37

2.5.4 Partitioning the Treatment Sum of Squares 37

2.5.5 Alternative Endpoints 38

2.5.6 Dummy Variables 38

2.5.7 Contrasts 39

2.6 Randomization 41

2.7 Hypotheses and Sample Size 41

2.8 Estimation and Analysis 41

2.9 Example 42

2.10 Discussion and Extensions 44

2.10.1 Two Roles for ANCOVA 44

2.10.2 Partitioning of Sums of Squares 45

2.10.3 Assumption of Parallelism 46

2.11 Notes 47

2.11.1 Constrained Randomization 47

2.11.2 Assumptions of the Analysis of Variance and Covariance 48

2.11.3 When the Assumptions Don't Hold 49

2.11.4 Alternative Graphical Displays 50

2.11.5 Sample Sizes for More Than Two Levels 51

2.11.6 Limitations of Computer Output 51

2.11.7 Unequal Sample Sizes 51

2.11.8 Design Implications of the CRD 51

2.11.9 Power and Alternative Hypotheses 52

2.11.10 Regression or Analysis of Variance? 52

2.11.11 Bioassay 52

2.12 Summary 53

2.13 Problems 53

3 Randomized Block Designs 63

3.1 Randomization 64

3.2 Hypotheses and Sample Size 64

3.3 Estimation and Analysis 64

3.4 Example 65

3.5 Discussion and Extensions 67

3.5.1 Evaluating Model Assumptions 67

3.5.2 Multiple Comparisons 69

3.5.3 Number of Treatments and Block Size 71

3.5.4 Missing Data 71

3.5.5 Does It Always Pay to Block? 71

3.5.6 Concomitant Variables 72

3.5.7 Imbalance 74

3.6 Randomization 77

3.7 Hypotheses and Sample Size 77

3.8 Estimation and Analysis 77

3.9 Example 77

3.10 Discussion and Extensions 79

3.10.1 Implications of the Model 79

3.10.2 Number of Latin Squares 79

3.11 Randomization 80

3.12 Hypotheses and Sample Size 81

3.13 Estimation and Analysis 82

3.14 Example 82

3.15 Discussion and Extensions 85

3.15.1 Partially Balanced Incomplete Block Designs 85

3.16 Notes 86

3.16.1 Analysis Follows Design 86

3.16.2 Relative Efficiency 86

3.16.3 Additivity of the Model 87

3.17 Summary 88

3.18 Problems 88

4 Factorial Designs 93

4.1 Randomization 95

4.2 Hypotheses and Sample Size 95

4.3 Estimation and Analysis 96<...