An Introduction to Optimal Designs for Social and Biomedical Research

, by Berger, Martijn P.F.; Wong, Weng-Kee

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

ISBN: 9780470694503 | 0470694505
Cover: Hardcover
Copyright: 6/29/2009

Rent

(Recommended)

$71.89

Term

Due

Price

Semester

Dec 12

$79.89

Quarter

Sep 1

$75.89

Short Term

Aug 13

$71.89

*This item is part of an exclusive publisher rental program and requires an additional convenience fee. This fee will be reflected in the shopping bag.
Buy New

Currently Available, Usually Ships in 24-48 Hours

$102.28
eBook

Available Instantly

Online: 1825 Days

Downloadable: Lifetime Access

*To support the delivery of the digital material to you, a digital delivery fee of $3.99 will be charged on each digital item.

$93.60*

This book makes the subject accessible with only basic knowledge of statistical methods and calculus required. Each chapter follows a similar, user-friendly pattern, introducing the statistical model, discussing design issues, and then developing the optimal design. Beginning by introducing the research, each subsequent chapter is dedicated to a specific design. The reader is guided from basic designs for simple linear models, through to designs for advanced repeated measures and nonlinear models. Practical examples and problems from relevant social and biomedical research further enhance the readerrs"s understanding, while web-based resources are provided in a final, summarizing chapter.

Preface	p. xi
Acknowledgements	p. xiii
Introduction to designs	p. 1
Introduction	p. 1
Stages of the research process	p. 4
Choice of a `good' design	p. 5
Research design	p. 6
Choice of independent variables and levels	p. 6
Units of analysis	p. 6
Variables	p. 7
Replication	p. 8
Types of research designs	p. 8
Requirements for a `good' design	p. 9
Statistical conclusion validity	p. 10
Internal validity	p. 12
Control of (unwanted) variation	p. 13
Ethical aspects of design choice	p. 16
Exact versus approximate designs	p. 17
Examples	p. 19
Radiation dosage example	p. 19
Designs for the Poggendorff and Ponzo illusion experiments	p. 20
Uncertainty about best fitting regression models	p. 22
Designs for a priori contrasts among composite faces	p. 23
Designs for calibration of item parameters in item response theory models	p. 24
Summary	p. 26
Designs for simple linear regression	p. 27
Design problem for a linear model	p. 27
The design	p. 28
The linear regression model	p. 31
Estimation of parameters and efficiency	p. 32
Designs for radiation-dosage example	p. 35
Relative efficiency and sample size	p. 36
Simultaneous inference	p. 37
Optimality criteria	p. 39
D-optimality criterion	p. 40
A-optimality criterion	p. 41
G-optimality criterion	p. 41
E-optimality criterion	p. 43
Number of distinct design points	p. 43
Relative efficiency	p. 44
Matrix formulation of designs for linear regression	p. 44
Summary	p. 49
Designs for multiple linear regression analysis	p. 51
Design problem for multiple linear regression	p. 51
The design	p. 52
The multiple linear regression model	p. 54
Estimation of parameters and efficiency	p. 54
Designs for vocabulary-growth study	p. 56
Relative efficiency and sample size	p. 60
Simultaneous inference	p. 61
Optimality criteria for a subset of parameters	p. 62
Relative efficiency	p. 64
Designs for polynomial regression model	p. 65
Exact D-optimal designs for a quadratic regression model	p. 69
Scale dependency of A- and E-optimality criteria	p. 71
The Poggendorff and Ponzo illusion study	p. 71
Uncertainty about best fitting regression models	p. 76
Matrix notation of designs for multiple regression models	p. 79
Design for regression models with two independent variables	p. 80
Design for regression models with two non-additive independent variables	p. 82
Summary	p. 85
Designs for analysis of variance models	p. 87
A typical design problem for an analysis of variance model	p. 87
The design	p. 89
The analysis of variance model	p. 90
Formulation of an ANOVA model as a regression model	p. 91
Estimation of parameters and efficiency	p. 95
Measures of uncertainty	p. 96
Simultaneous inference and optimality criteria	p. 97
Designs for groups under stress study	p. 98
A priori planned unequal sample sizes	p. 99
Not planned unequal sample sizes	p. 100
Specific hypotheses and contrasts	p. 101
Loss of efficiency and power	p. 103
Designs for the composite faces study	p. 106
Balanced designs versus unbalanced designs	p. 109
Matrix notation for Groups under Stress study	p. 109
Summary	p. 111
Designs for logistic regression models	p. 113
Design problem for logistic regression	p. 113
The design	p. 114
The logistic regression model	p. 115
Design for a single dichotomous independent variable	p. 116
Design for multiple qualitative independent variables	p. 122
Design for a single quantitative independent variable	p. 125
Design for two independent quantitative variables	p. 130
Approaches to deal with local optimality	p. 133
Designs for calibration of item parameters in item response theory models	p. 134
Matrix formulation of designs for logistic regression	p. 137
Hours of practice experiment	p. 138
Problem solving study	p. 140
Summary	p. 141
Designs for multilevel models	p. 143
Design problem for multilevel models	p. 143
The design	p. 144
Validity considerations	p. 146
The multilevel regression model	p. 147
Cluster randomization of treatment	p. 147
Subject randomization of treatment	p. 149
Cluster versus subject randomization	p. 151
Cost function	p. 153
Example: Nursing home study	p. 155
Cluster randomization	p. 157
Subject randomization	p. 159
Optimal design and power	p. 160
Power for cluster randomized design	p. 162
Power for multi-center design	p. 164
Increase of efficiency and power by including covariates	p. 165
Unequal sample sizes	p. 165
Design effect in multilevel surveys	p. 166
Values of intra-class correlation ¿	p. 168
Cluster randomized sampling versus simple random sampling	p. 168
Matrix formulation of the multilevel model	p. 169
Cluster randomization of treatment	p. 170
Subject randomization of treatment	p. 172
Summary	p. 174
Longitudinal designs for repeated measurement models	p. 175
Design problem for repeated measurements	p. 175
The design	p. 179
Analysis techniques for repeated measures	p. 180
The linear mixed effects model for repeated measurement data	p. 181
Random intercept model	p. 182
Random intercept and slope model	p. 183
Variance-covariance structures	p. 184
Compound symmetry structure	p. 184
Auto-correlation structure	p. 185
Estimation of parameters and efficiency	p. 187
Small sample behaviour of estimators	p. 188
Bone mineral density example	p. 189
Improvement of the longitudinal design	p. 194
Cost function	p. 196
D-optimal designs for linear mixed effects models with autocorrelated errors	p. 200
Miscellanea	p. 207
Homoscedasticity	p. 207
Uninformative dropout	p. 208
Matrix formulation of the linear mixed effects model	p. 208
Summary	p. 211
Two-treatment crossover designs	p. 213
Design problem for crossover studies	p. 213
The design	p. 216
Confounding treatment effects with nuisance effects	p. 218
The linear model for crossover designs	p. 221
Estimation of parameters and efficiency	p. 223
Cost and efficiency of the crossover design	p. 223
Cost function	p. 226
Optimal crossover designs for two treatments	p. 229
Some further observations	p. 231
Matrix formulation of the mixed model for crossover designs	p. 232
Summary	p. 235
Alternative optimal designs for linear models	p. 237
Introduction	p. 237
Information matrix	p. 238
D_A- or D_s-optimal designs	p. 239
Extrapolation optimal design	p. 241
L-optimal designs	p. 242
Bayesian optimal designs	p. 244
Minimax optimal design	p. 247
Multiple-objective optimal designs	p. 250
Constrained optimal design	p. 251
Compound optimal design	p. 253
Summary	p. 255
Optimal designs for nonlinear models	p. 257
Introduction	p. 257
Linear models versus nonlinear models	p. 258
The Arrhenius equation	p. 258
The compartmental model	p. 259
The Michaelis-Menten model	p. 260
The Emax model	p. 261
Design issues for nonlinear models	p. 261
Local optimality	p. 262
Alternative optimal designs with examples	p. 265
D_A or D_s-optimal design	p. 265
Extrapolation optimal design	p. 266
Optimal design for estimating percentiles	p. 266
Bayesian optimal designs	p. 267
Minimax optimal design	p. 269
Multiple-objective optimal designs	p. 271
Optimal design for model discrimination	p. 273
Summary	p. 275
Resources for the construction of optimal designs	p. 277
Introduction	p. 277
Sequential construction of optimal designs	p. 278
Exchange of design points	p. 283
Exchange algorithms	p. 283
Other algorithms	p. 284
Optimal design software	p. 285
A web site for finding optimal designs	p. 286
Optimal designs for the Michaelis-Menten and Emax models	p. 288
Optimal designs for discriminating among toxicological models	p. 290
Summary	p. 294
References	p. 295
Author Index	p. 313
Subject Index	p. 319
Table of Contents provided by Ingram. All Rights Reserved.

Please wait while the item is added to your bag...

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

An Introduction to Optimal Designs for Social and Biomedical Research

Summary

Author Biography

Table of Contents

Supplemental Materials

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

An Introduction to Optimal Designs for Social and Biomedical Research

Summary

Author Biography

Table of Contents

Supplemental Materials

Digital License