Design and Analysis of Experiments in the

Design Rules! A Primer on Laboratory Experiments presents the principles and regulations that apply to most areas of research, including clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies. The book explores two theories: that key concepts of experimental designs can be illustrated using (at most) two factors with two treatment levels per factor; and that design of experiments and analysis of data from experiments are tightly linked. This primer is essential for graduate students, environmental consultants, and professionals who need a hands-on book for designing and analyzing laboratory experiments.

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.

The Basics	p. 1
Four Basic Questions	p. 1
Variation	p. 5
Principles of Design and Analysis	p. 6
Experiments and observational studies	p. 11
Illustrative applications of principles	p. 14
Experiments in the health sciences	p. 15
Adaptive Allocation	p. 19
Sample Size Calculations	p. 23
Statistical models for the data	p. 25
Analysis and presentation	p. 27
Notes	p. 31
Summary	p. 33
Problems	p. 34
Completely Randomized Experiments	p. 41
Randomization	p. 42
Hypotheses and Sample Size	p. 42
Estimation and Analysis	p. 43
Example	p. 44
Discussion and Extensions	p. 46
Randomization	p. 54
Hypotheses and Sample Size	p. 54
Estimation and Analysis	p. 54
Example	p. 55
Discussion and Extensions	p. 59
Notes	p. 61
Summary	p. 70
Problems	p. 70
Randomized Block Designs	p. 83
Randomization	p. 84
Hypotheses and Sample Size	p. 84
Estimation and Analysis	p. 85
Example	p. 85
Discussion and Extensions	p. 89
Randomization	p. 99
Hypotheses and Sample Size	p. 99
Estimation and Analysis	p. 99
Example	p. 100
Discussion and Extensions	p. 102
Randomization	p. 104
Hypotheses and Sample Size	p. 104
Estimation and Analysis	p. 106
Example	p. 106
Discussion and Extensions	p. 110
Notes	p. 111
Summary	p. 113
Problems	p. 114
Factorial Designs	p. 121
Randomization	p. 123
Hypotheses and Sample Size	p. 124
Estimation and Analysis	p. 125
Example 1	p. 126
Example 2	p. 130
Notes	p. 132
Summary	p. 142
Problems	p. 142
Multilevel Experiments	p. 149
Randomization	p. 151
Hypotheses and Sample Size	p. 151
Estimation and Analysis	p. 152
Example	p. 154
Discussion and Extensions	p. 162
Notes	p. 164
Summary	p. 165
Problems	p. 166
Repeated Measures Designs	p. 171
Randomization	p. 172
Hypotheses and Sample Size	p. 173
Estimation and Analysis	p. 173
Example	p. 176
Discussion and Extensions	p. 179
Notes	p. 182
Summary	p. 184
Problems	p. 184
Randomized Clinical Trials	p. 191
Endpoints	p. 194
Randomization	p. 196
Hypotheses and Sample Size	p. 197
Follow-up	p. 198
Estimation and Analysis	p. 198
Examples	p. 200
Discussion and Extensions	p. 205
Notes	p. 214
Resources	p. 220
Summary	p. 220
Problems	p. 221
Microarrays	p. 231
Introduction	p. 231
Genes, Gene Expression and Microarrays	p. 231
Examples of microarray studies	p. 240
Replication and sample size	p. 242
Blocking and microarrays	p. 244
Randomization and microarrays	p. 246
Microarray data analysis issues	p. 247
Data analysis example	p. 259
Notes	p. 262
Summary	p. 263
Problems	p. 263
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Design and Analysis of Experiments in the Health Sciences

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