Details

Sample Size Determination and Power

Wiley Series in Probability and Statistics 1. Aufl.

von: Thomas P. Ryan
106,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	28.05.2013
ISBN/EAN:	9781118439227
Sprache:	englisch
Anzahl Seiten:	400

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Beschreibungen

Titelbeschreibung

A comprehensive approach to sample size determination and power with applications for a variety of fields Sample Size Determination and Power features a modern introduction to the applicability of sample size determination and provides a variety of discussions on broad topics including epidemiology, microarrays, survival analysis and reliability, design of experiments, regression, and confidence intervals. The book distinctively merges applications from numerous fields such as statistics, biostatistics, the health sciences, and engineering in order to provide a complete introduction to the general statistical use of sample size determination. Advanced topics including multivariate analysis, clinical trials, and quality improvement are addressed, and in addition, the book provides considerable guidance on available software for sample size determination. Written by a well-known author who has extensively class-tested the material, Sample Size Determination and Power: <ul> <li>Highlights the applicability of sample size determination and provides extensive literature coverage</li> <li>Presents a modern, general approach to relevant software to guide sample size determination including CATD (computer-aided trial design)</li> <li>Addresses the use of sample size determination in grant proposals and provides up-to-date references for grant investigators</li> </ul> An appealing reference book for scientific researchers in a variety of fields, such as statistics, biostatistics, the health sciences, mathematics, ecology, and geology, who use sampling and estimation methods in their work, Sample Size Determination and Power is also an ideal supplementary text for upper-level undergraduate and graduate-level courses in statistical sampling.

Inhaltsverzeichnis

Preface xv 1 Brief Review of Hypothesis Testing Concepts/Issues and Confidence Intervals 1 1.1 Basic Concepts of Hypothesis Testing, 1 1.2 Review of Confidence Intervals and Their Relationship to Hypothesis Tests, 5 1.3 Sports Applications, 9 1.4 Observed Power, Retrospective Power, Conditional Power, and Predictive Power, 9 1.5 Testing for Equality, Equivalence, Noninferiority, or Superiority, 10 1.5.1 Software, 11 References, 12 Exercises, 14 2 Methods of Determining Sample Sizes 17 2.1 Internal Pilot Study Versus External Pilot Study, 20 2.2 Examples: Frequentist and Bayesian, 24 2.2.1 Bayesian Approaches, 30 2.2.2 Probability Assessment Approach, 31 2.2.3 Reproducibility Probability Approach, 32 2.2.4 Competing Probability Approach, 32 2.2.5 Evidential Approach, 32 2.3 Finite Populations, 32 2.4 Sample Sizes for Confidence Intervals, 33 2.4.1 Using the Finite Population Correction Factor, 36 2.4.1.1 Estimating Population Totals, 38 2.5 Confidence Intervals on Sample Size and Power, 39 2.6 Specification of Power, 39 2.7 Cost of Sampling, 40 2.8 Ethical Considerations, 40 2.9 Standardization and Specification of Effect Sizes, 42 2.10 Equivalence Tests, 43 2.11 Software and Applets, 45 2.12 Summary, 47 References, 47 Exercises, 53 3 Means and Variances 57 3.1 One Mean, Normality, and Known Standard Deviation, 58 3.1.1 Using the Coefficient of Variation, 65 3.2 One Mean, Standard Deviation Unknown, Normality Assumed, 66 3.3 Confidence Intervals on Power and/or Sample Size, 67 3.4 One Mean, Standard Deviation Unknown, Nonnormality Assumed, 70 3.5 One Mean, Exponential Distribution, 71 3.6 Two Means, Known Standard Deviations—Independent Samples, 71 3.6.1 Unequal Sample Sizes, 74 3.7 Two Means, Unknown but Equal Standard Deviations—Independent Samples, 74 3.7.1 Unequal Sample Sizes, 76 3.8 Two Means, Unequal Variances and Sample Sizes—Independent Samples, 77 3.9 Two Means, Unknown and Unequal Standard Deviations—Independent Samples, 77 3.10 Two Means, Known and Unknown Standard Deviations—Dependent Samples, 78 3.11 Bayesian Methods for Comparing Means, 81 3.12 One Variance or Standard Deviation, 81 3.13 Two Variances, 83 3.14 More Than Two Variances, 84 3.15 Confidence Intervals, 84 3.15.1 Adaptive Confidence Intervals, 85 3.15.2 One Mean, Standard Deviation Unknown—With Tolerance Probability, 85 3.15.3 Difference Between Two Independent Means, Standard Deviations Known and Unknown—With and Without Tolerance Probability, 88 3.15.4 Difference Between Two Paired Means, 90 3.15.5 One Variance, 91 3.15.6 One-Sided Confidence Bounds, 92 3.16 Relative Precision, 93 3.17 Computing Aids, 94 3.18 Software, 94 3.19 Summary, 95 Appendix, 95 References, 96 Exercises, 99 4 Proportions and Rates 103 4.1 One Proportion, 103 4.1.1 One Proportion—With Continuity Correction, 107 4.1.2 Software Disagreement and Rectification, 108 4.1.3 Equivalence Tests and Noninferiority Tests for One Proportion, 109 4.1.4 Confidence Interval and Error of Estimation, 110 4.1.5 One Proportion—Exact Approach, 113 4.1.6 Bayesian Approaches, 115 4.2 Two Proportions, 115 4.2.1 Two Proportions—With Continuity Correction, 119 4.2.2 Two Proportions—Fisher’s Exact Test, 121 4.2.3 What Approach Is Recommended?, 122 4.2.4 Correlated Proportions, 123 4.2.5 Equivalence Tests for Two Proportions, 124 4.2.6 Noninferiority Tests for Two Proportions, 125 4.2.7 Need for Pilot Study?, 125 4.2.8 Linear Trend in Proportions, 125 4.2.9 Bayesian Method for Estimating the Difference of Two Binomial Proportions, 126 4.3 Multiple Proportions, 126 4.4 Multinomial Probabilities and Distributions, 129 4.5 One Rate, 130 4.5.1 Pilot Study Needed?, 132 4.6 Two Rates, 132 4.7 Bayesian Sample Size Determination Methods for Rates, 135 4.8 Software, 135 4.9 Summary, 136 Appendix, 136 References, 140 Exercises, 144 5 Regression Methods and Correlation 145 5.1 Linear Regression, 145 5.1.1 Simple Linear Regression, 146 5.1.2 Multiple Linear Regression, 150 5.1.2.1 Application: Predicting College Freshman Grade Point Average, 155 5.2 Logistic Regression, 155 5.2.1 Simple Logistic Regression, 156 5.2.1.1 Normally Distributed Covariate, 158 5.2.1.2 Binary Covariate, 162 5.2.2 Multiple Logistic Regression, 163 5.2.2.1 Measurement Error, 165 5.2.3 Polytomous Logistic Regression, 165 5.2.4 Ordinal Logistic Regression, 166 5.2.5 Exact Logistic Regression, 167 5.3 Cox Regression, 167 5.4 Poisson Regression, 169 5.5 Nonlinear Regression, 172 5.6 Other Types of Regression Models, 172 5.7 Correlation, 172 5.7.1 Confidence Intervals, 174 5.7.2 Intraclass Correlation, 175 5.7.3 Two Correlations, 175 5.8 Software, 176 5.9 Summary, 177 References, 177 Exercises, 180 6 Experimental Designs 183 6.1 One Factor—Two Fixed Levels, 184 6.1.1 Unequal Sample Sizes, 186 6.2 One Factor—More Than Two Fixed Levels, 187 6.2.1 Multiple Comparisons and Dunnett’s Test, 192 6.2.2 Analysis of Means (ANOM), 193 6.2.3 Unequal Sample Sizes, 195 6.2.4 Analysis of Covariance, 196 6.2.5 Randomized Complete Block Designs, 197 6.2.6 Incomplete Block Designs, 198 6.2.7 Latin Square Designs, 199 6.2.7.1 Graeco-Latin Square Designs, 202 6.3 Two Factors, 203 6.4 2k Designs, 205 6.4.1 22 Design with Equal and Unequal Variances, 206 6.4.2 Unreplicated 2k Designs, 206 6.4.3 Software for 2k Designs, 208 6.5 2k − p Designs, 209 6.6 Detecting Conditional Effects, 210 6.7 General Factorial Designs, 211 6.8 Repeated Measures Designs, 212 6.8.1 Crossover Designs, 215 6.8.1.1 Software, 217 6.9 Response Surface Designs, 218 6.10 Microarray Experiments, 219 6.10.1 Software, 220 6.11 Other Designs, 220 6.11.1 Plackett–Burman Designs, 220 6.11.2 Split-Plot and Strip-Plot Designs, 222 6.11.3 Nested Designs, 224 6.11.4 Ray designs, 225 6.12 Designs for Nonnormal Responses, 225 6.13 Designs with Random Factors, 227 6.14 Zero Patient Design, 228 6.15 Computer Experiments, 228 6.16 Noninferiority and Equivalence Designs, 229 6.17 Pharmacokinetic Experiments, 229 6.18 Bayesian Experimental Design, 229 6.19 Software, 230 6.20 Summary, 232 Appendix, 233 References, 234 Exercises, 239 7 Clinical Trials 243 7.1 Clinical Trials, 245 7.1.1 Cluster Randomized Trials, 247 7.1.2 Phase II Trials, 247 7.1.2.1 Phase II Cancer Trials, 247 7.1.3 Phase III Trials, 247 7.1.4 Longitudinal Clinical Trials, 248 7.1.5 Fixed Versus Adaptive Clinical Trials, 248 7.1.6 Noninferiority Trials, 249 7.1.7 Repeated Measurements, 249 7.1.8 Multiple Tests, 250 7.1.9 Use of Internal Pilot Studies for Clinical Trials, 250 7.1.10 Using Historical Controls, 250 7.1.11 Trials with Combination Treatments, 251 7.1.12 Group Sequential Trials, 251 7.1.13 Vaccine Efficacy Studies, 251 7.2 Bioequivalence Studies, 251 7.3 Ethical Considerations, 252 7.4 The Use of Power in Clinical Studies, 252 7.5 Preclinical Experimentation, 253 7.6 Pharmacodynamic, Pharmacokinetic, and Pharmacogenetic Experiments, 253 7.7 Method of Competing Probability, 254 7.8 Bayesian Methods, 255 7.9 Cost and Other Sample Size Determination Methods for Clinical Trials, 256 7.10 Meta-Analyses of Clinical Trials, 256 7.11 Miscellaneous, 257 7.12 Survey Results of Published Articles, 259 7.13 Software, 260 7.14 Summary, 263 References, 263 Exercises, 275 8 Quality Improvement 277 8.1 Control Charts, 277 8.1.1 Shewhart Measurement Control Charts, 278 8.1.2 Using Software to Determine Subgroup Size, 281 8.1.2.1 ¯X -Chart, 282 8.1.2.2 S-Chart and S2-Chart, 284 8.1.3 Attribute Control Charts, 286 8.1.4 CUSUM and EWMA Charts, 289 8.1.4.1 Subgroup Size Considerations for CUSUM Charts, 290 8.1.4.2 CUSUM and EWMA Variations, 291 8.1.4.3 Subgroup Size Determination for CUSUM and EWMA Charts and Their Variations, 291 8.1.4.4 EWMA Applied to Autocorrelated Data, 293 8.1.5 Adaptive Control Charts, 293 8.1.6 Regression and Cause-Selecting Control Charts, 293 8.1.7 Multivariate Control Charts, 295 8.2 Medical Applications, 296 8.3 Process Capability Indices, 297 8.4 Tolerance Intervals, 298 8.5 Measurement System Appraisal, 300 8.6 Acceptance Sampling, 300 8.7 Reliability and Life Testing, 301 8.8 Software, 301 8.9 Summary, 302 References, 302 Exercises, 305 9 Survival Analysis and Reliability 307 9.1 Survival Analysis, 307 9.1.1 Logrank Test, 308 9.1.1.1 Freedman Method, 311 9.1.1.2 Other Methods, 312 9.1.2 Wilcoxon–Breslow–Gehan Test, 313 9.1.3 Tarone–Ware Test, 313 9.1.4 Other Tests, 314 9.1.5 Cox Proportional Hazards Model, 314 9.1.6 Joint Modeling of Longitudinal and Survival Data, 315 9.1.7 Multistage Designs, 316 9.1.8 Comparison of Software and Freeware, 316 9.2 Reliability Analysis, 317 9.3 Summary, 318 References, 319 Exercise, 321 10 Nonparametric Methods 323 10.1 Wilcoxon One-Sample Test, 324 10.1.1 Wilcoxon Test for Paired Data, 327 10.2 Wilcoxon Two–Sample Test (Mann–Whitney Test), 327 10.2.1 van Elteren Test—A Stratified Mann–Whitney Test, 331 10.3 Kruskal–Wallis One-Way ANOVA, 331 10.4 Sign Test, 331 10.5 McNemar’s Test, 334 10.6 Contingency Tables, 334 10.7 Quasi-Likelihood Method, 334 10.8 Rank Correlation Coefficients, 335 10.9 Software, 335 10.10 Summary, 336 References, 336 Exercises, 339 11 Miscellaneous Topics 341 11.1 Case–Control Studies, 341 11.2 Epidemiology, 342 11.3 Longitudinal Studies, 342 11.4 Microarray Studies, 343 11.5 Receiver Operating Characteristic ROC Curves, 343 11.6 Meta-Analyses, 343 11.7 Sequential Sample Sizes, 343 11.8 Sample Surveys, 344 11.8.1 Vegetation Surveys, 344 11.9 Cluster Sampling, 345 11.10 Factor Analysis, 346 11.11 Multivariate Analysis of Variance and Other Multivariate Methods, 346 11.12 Structural Equation Modeling, 348 11.13 Multilevel Modeling, 349 11.14 Prediction Intervals, 349 11.15 Measures of Agreement, 350 11.16 Spatial Statistics, 350 11.17 Agricultural Applications, 350 11.18 Estimating the Number of Unseen Species, 351 11.19 Test Reliability, 351 11.20 Agreement Studies, 351 11.21 Genome-wide Association Studies, 351 11.22 National Security, 352 11.23 Miscellaneous, 352 11.24 Summary, 353 References, 354 Answers to Selected Exercises 363 Index 369

Rezension

“This very comprehensive book is well structured.” (International Statistical Review, 1 October 2015) “In summary, this book succeeds in the author’s aim of providing a general purpose text for readers interested in methodology without much technical fuss. In general, the book focuses on providing wealth of discussions and reviews for sample size and power and is an excellent source for up-to-date software and references for a wide range of topics related to sample size determination.” (International Statistical Review, 1 March 2015)

Autorenportrait

THOMAS P. RYAN, PhD, teaches online advanced statistics courses for Northwestern University and The Institute for Statistics Education in sample size determination, design of experiments, engineering statistics, and regression analysis.

Back cover copy

A comprehensive approach to sample size determination and power with applications for a variety of fields Sample Size Determination and Power features a modern introduction to the applicability of sample size determination and provides a variety of discussions on broad topics including epidemiology, microarrays, survival analysis and reliability, design of experiments, regression, and confidence intervals. The book distinctively merges applications from numerous fields such as statistics, biostatistics, the health sciences, and engineering in order to provide a complete introduction to the general statistical use of sample size determination. Advanced topics including multivariate analysis, clinical trials, and quality improvement are addressed, and in addition, the book provides considerable guidance on available software for sample size determination. Written by a well-known author who has extensively class-tested the material, Sample Size Determination and Power: <ul> <li>Highlights the applicability of sample size determination and provides extensive literature coverage</li> <li>Presents a modern, general approach to relevant software to guide sample size determination including CATD (computer-aided trial design)</li> <li>Addresses the use of sample size determination in grant proposals and provides up-to-date references for grant investigators</li> </ul> An appealing reference book for scientific researchers in a variety of fields, such as statistics, biostatistics, the health sciences, mathematics, ecology, and geology, who use sampling and estimation methods in their work, Sample Size Determination and Power is also an ideal supplementary text for upper-level undergraduate and graduate-level courses in statistical sampling.