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El. knyga: Sample Size Determination and Power

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(University of Iowa)
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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:





Highlights the applicability of sample size determination and provides extensive literature coverage Presents a modern, general approach to relevant software to guide sample size determination including CATD (computer-aided trial design) Addresses the use of sample size determination in grant proposals and provides up-to-date references for grant investigators

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.

Recenzijos

This very comprehensive book is well structured.  (International Statistical Review, 1 October 2015)

In summary, this book succeeds in the authors 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)

Preface xv
1 Brief Review of Hypothesis Testing Concepts/Issues and Confidence Intervals
1(16)
1.1 Basic Concepts of Hypothesis Testing
1(4)
1.2 Review of Confidence Intervals and Their Relationship to Hypothesis Tests
5(4)
1.3 Sports Applications
9(1)
1.4 Observed Power, Retrospective Power, Conditional Power, and Predictive Power
9(1)
1.5 Testing for Equality, Equivalence, Noninferiority, or Superiority
10(7)
1.5.1 Software
11(1)
References
12(2)
Exercises
14(3)
2 Methods of Determining Sample Sizes
17(40)
2.1 Internal Pilot Study Versus External Pilot Study
20(4)
2.2 Examples: Frequentist and Bayesian
24(8)
2.2.1 Bayesian Approaches
30(1)
2.2.2 Probability Assessment Approach
31(1)
2.2.3 Reproducibility Probability Approach
32(1)
2.2.4 Competing Probability Approach
32(1)
2.2.5 Evidential Approach
32(1)
2.3 Finite Populations
32(1)
2.4 Sample Sizes for Confidence Intervals
33(6)
2.4.1 Using the Finite Population Correction Factor
36(2)
2.4.1.1 Estimating Population Totals
38(1)
2.5 Confidence Intervals on Sample Size and Power
39(1)
2.6 Specification of Power
39(1)
2.7 Cost of Sampling
40(1)
2.8 Ethical Considerations
40(2)
2.9 Standardization and Specification of Effect Sizes
42(1)
2.10 Equivalence Tests
43(2)
2.11 Software and Applets
45(2)
2.12 Summary
47(10)
References
47(6)
Exercises
53(4)
3 Means and Variances
57(46)
3.1 One Mean, Normality, and Known Standard Deviation
58(8)
3.1.1 Using the Coefficient of Variation
65(1)
3.2 One Mean, Standard Deviation Unknown, Normality Assumed
66(1)
3.3 Confidence Intervals on Power and/or Sample Size
67(3)
3.4 One Mean, Standard Deviation Unknown, Nonnormality Assumed
70(1)
3.5 One Mean, Exponential Distribution
71(1)
3.6 Two Means, Known Standard Deviations---Independent Samples
71(3)
3.6.1 Unequal Sample Sizes
74(1)
3.7 Two Means, Unknown but Equal Standard Deviations---Independent Samples
74(3)
3.7.1 Unequal Sample Sizes
76(1)
3.8 Two Means, Unequal Variances and Sample Sizes---Independent Samples
77(1)
3.9 Two Means, Unknown and Unequal Standard Deviations---Independent Samples
77(1)
3.10 Two Means, Known and Unknown Standard Deviations---Dependent Samples
78(3)
3.11 Bayesian Methods for Comparing Means
81(1)
3.12 One Variance or Standard Deviation
81(2)
3.13 Two Variances
83(1)
3.14 More Than Two Variances
84(1)
3.15 Confidence Intervals
84(9)
3.15.1 Adaptive Confidence Intervals
85(1)
3.15.2 One Mean, Standard Deviation Unknown---With Tolerance Probability
85(3)
3.15.3 Difference Between Two Independent Means, Standard Deviations Known and Unknown---With and Without Tolerance Probability
88(2)
3.15.4 Difference Between Two Paired Means
90(1)
3.15.5 One Variance
91(1)
3.15.6 One-Sided Confidence Bounds
92(1)
3.16 Relative Precision
93(1)
3.17 Computing Aids
94(1)
3.18 Software
94(1)
3.19 Summary
95(8)
Appendix
95(1)
References
96(3)
Exercises
99(4)
4 Proportions and Rates
103(42)
4.1 One Proportion
103(12)
4.1.1 One Proportion---With Continuity Correction
107(1)
4.1.2 Software Disagreement and Rectification
108(1)
4.1.3 Equivalence Tests and Noninferiority Tests for One Proportion
109(1)
4.1.4 Confidence Interval and Error of Estimation
110(3)
4.1.5 One Proportion--Exact Approach
113(2)
4.1.6 Bayesian Approaches
115(1)
4.2 Two Proportions
115(11)
4.2.1 Two Proportions---With Continuity Correction
119(2)
4.2.2 Two Proportions---Fisher's Exact Test
121(1)
4.2.3 What Approach Is Recommended?
122(1)
4.2.4 Correlated Proportions
123(1)
4.2.5 Equivalence Tests for Two Proportions
124(1)
4.2.6 Noninferiority Tests for Two Proportions
125(1)
4.2.7 Need for Pilot Study?
125(1)
4.2.8 Linear Trend in Proportions
125(1)
4.2.9 Bayesian Method for Estimating the Difference of Two Binomial Proportions
126(1)
4.3 Multiple Proportions
126(3)
4.4 Multinomial Probabilities and Distributions
129(1)
4.5 One Rate
130(2)
4.5.1 Pilot Study Needed?
132(1)
4.6 Two Rates
132(3)
4.7 Bayesian Sample Size Determination Methods for Rates
135(1)
4.8 Software
135(1)
4.9 Summary
136(9)
Appendix
136(4)
References
140(4)
Exercises
144(1)
5 Regression Methods and Correlation
145(38)
5.1 Linear Regression
145(10)
5.1.1 Simple Linear Regression
146(4)
5.1.2 Multiple Linear Regression
150(5)
5.1.2.1 Application: Predicting College Freshman Grade Point Average
155(1)
5.2 Logistic Regression
155(12)
5.2.1 Simple Logistic Regression
156(2)
5.2.1.1 Normally Distributed Covariate
158(4)
5.2.1.2 Binary Covariate
162(1)
5.2.2 Multiple Logistic Regression
163(2)
5.2.2.1 Measurement Error
165(1)
5.2.3 Polytomous Logistic Regression
165(1)
5.2.4 Ordinal Logistic Regression
166(1)
5.2.5 Exact Logistic Regression
167(1)
5.3 Cox Regression
167(2)
5.4 Poisson Regression
169(3)
5.5 Nonlinear Regression
172(1)
5.6 Other Types of Regression Models
172(1)
5.7 Correlation
172(4)
5.7.1 Confidence Intervals
174(1)
5.7.2 Intraclass Correlation
175(1)
5.7.3 Two Correlations
175(1)
5.8 Software
176(1)
5.9 Summary
177(6)
References
177(3)
Exercises
180(3)
6 Experimental Designs
183(60)
6.1 One Factor---Two Fixed Levels
184(3)
6.1.1 Unequal Sample Sizes
186(1)
6.2 One Factor---More Than Two Fixed Levels
187(16)
6.2.1 Multiple Comparisons and Dunnett's Test
192(1)
6.2.2 Analysis of Means (ANOM)
193(2)
6.2.3 Unequal Sample Sizes
195(1)
6.2.4 Analysis of Covariance
196(1)
6.2.5 Randomized Complete Block Designs
197(1)
6.2.6 Incomplete Block Designs
198(1)
6.2.7 Latin Square Designs
199(3)
6.2.7.1 Graeco-Latin Square Designs
202(1)
6.3 Two Factors
203(2)
6.4 2k Designs
205(4)
6.4.1 22 Design with Equal and Unequal Variances
206(1)
6.4.2 Unreplicated 2k Designs
206(2)
6.4.3 Software for 2k Designs
208(1)
6.5 2k--p Designs
209(1)
6.6 Detecting Conditional Effects
210(1)
6.7 General Factorial Designs
211(1)
6.8 Repeated Measures Designs
212(6)
6.8.1 Crossover Designs
215(2)
6.8.1.1 Software
217(1)
6.9 Response Surface Designs
218(1)
6.10 Microarray Experiments
219(1)
6.10.1 Software
220(1)
6.11 Other Designs
220(5)
6.11.1 Plackett-Burman Designs
220(2)
6.11.2 Split-Plot and Strip-Plot Designs
222(2)
6.11.3 Nested Designs
224(1)
6.11.4 Ray designs
225(1)
6.12 Designs for Nonnormal Responses
225(2)
6.13 Designs with Random Factors
227(1)
6.14 Zero Patient Design
228(1)
6.15 Computer Experiments
228(1)
6.16 Noninferiority and Equivalence Designs
229(1)
6.17 Pharmacokinetic Experiments
229(1)
6.18 Bayesian Experimental Design
229(1)
6.19 Software
230(2)
6.20 Summary
232(11)
Appendix
233(1)
References
234(5)
Exercises
239(4)
7 Clinical Trials
243(34)
7.1 Clinical Trials
245(6)
7.1.1 Cluster Randomized Trials
247(1)
7.1.2 Phase II Trials
247(1)
7.1.2.1 Phase II Cancer Trials
247(1)
7.1.3 Phase III Trials
247(1)
7.1.4 Longitudinal Clinical Trials
248(1)
7.1.5 Fixed Versus Adaptive Clinical Trials
248(1)
7.1.6 Noninferiority Trials
249(1)
7.1.7 Repeated Measurements
249(1)
7.1.8 Multiple Tests
250(1)
7.1.9 Use of Internal Pilot Studies for Clinical Trials
250(1)
7.1.10 Using Historical Controls
250(1)
7.1.11 Trials with Combination Treatments
251(1)
7.1.12 Group Sequential Trials
251(1)
7.1.13 Vaccine Efficacy Studies
251(1)
7.2 Bioequivalence Studies
251(1)
7.3 Ethical Considerations
252(1)
7.4 The Use of Power in Clinical Studies
252(1)
7.5 Preclinical Experimentation
253(1)
7.6 Pharmacodynamic, Pharmacokinetic, and Pharmacogenetic Experiments
253(1)
7.7 Method of Competing Probability
254(1)
7.8 Bayesian Methods
255(1)
7.9 Cost and Other Sample Size Determination Methods for Clinical Trials
256(1)
7.10 Meta-Analyses of Clinical Trials
256(1)
7.11 Miscellaneous
257(2)
7.12 Survey Results of Published Articles
259(1)
7.13 Software
260(3)
7.14 Summary
263(14)
References
263(12)
Exercises
275(2)
8 Quality Improvement
277(30)
8.1 Control Charts
277(19)
8.1.1 Shewhart Measurement Control Charts
278(3)
8.1.2 Using Software to Determine Subgroup Size
281(1)
8.1.2.1 X-Chart
282(2)
8.1.2.2 S-Chart and S2-Chart
284(2)
8.1.3 Attribute Control Charts
286(3)
8.1.4 CUSUM and EWMA Charts
289(1)
8.1.4.1 Subgroup Size Considerations for CUSUM Charts
290(1)
8.1.4.2 CUSUM and EWMA Variations
291(1)
8.1.4.3 Subgroup Size Determination for CUSUM and EWMA Charts and Their Variations
291(2)
8.1.4.4 EWMA Applied to Autocorrelated Data
293(1)
8.1.5 Adaptive Control Charts
293(1)
8.1.6 Regression and Cause-Selecting Control Charts
293(2)
8.1.7 Multivariate Control Charts
295(1)
8.2 Medical Applications
296(1)
8.3 Process Capability Indices
297(1)
8.4 Tolerance Intervals
298(2)
8.5 Measurement System Appraisal
300(1)
8.6 Acceptance Sampling
300(1)
8.7 Reliability and Life Testing
301(1)
8.8 Software
301(1)
8.9 Summary
302(5)
References
302(3)
Exercises
305(2)
9 Survival Analysis and Reliability
307(16)
9.1 Survival Analysis
307(10)
9.1.1 Logrank Test
308(3)
9.1.1.1 Freedman Method
311(1)
9.1.1.2 Other Methods
312(1)
9.1.2 Wilcoxon-Breslow-Gehan Test
313(1)
9.1.3 Tarone-Ware Test
313(1)
9.1.4 Other Tests
314(1)
9.1.5 Cox Proportional Hazards Model
314(1)
9.1.6 Joint Modeling of Longitudinal and Survival Data
315(1)
9.1.7 Multistage Designs
316(1)
9.1.8 Comparison of Software and Freeware
316(1)
9.2 Reliability Analysis
317(1)
9.3 Summary
318(5)
References
319(2)
Exercise
321(2)
10 Nonparametric Methods
323(18)
10.1 Wilcoxon One-Sample Test
324(3)
10.1.1 Wilcoxon Test for Paired Data
327(1)
10.2 Wilcoxon Two-Sample Test (Mann-Whitney Test)
327(4)
10.2.1 van Elteren Test---A Stratified Mann-Whitney Test
331(1)
10.3 Kruskal--Wallis One-Way ANOVA
331(1)
10.4 Sign Test
331(3)
10.5 McNemar's Test
334(1)
10.6 Contingency Tables
334(1)
10.7 Quasi-Likelihood Method
334(1)
10.8 Rank Correlation Coefficients
335(1)
10.9 Software
335(1)
10.10 Summary
336(5)
References
336(3)
Exercises
339(2)
11 Miscellaneous Topics
341(22)
11.1 Case-Control Studies
341(1)
11.2 Epidemiology
342(1)
11.3 Longitudinal Studies
342(1)
11.4 Microarray Studies
343(1)
11.5 Receiver Operating Characteristic ROC Curves
343(1)
11.6 Meta-Analyses
343(1)
11.7 Sequential Sample Sizes
343(1)
11.8 Sample Surveys
344(1)
11.8.1 Vegetation Surveys
344(1)
11.9 Cluster Sampling
345(1)
11.10 Factor Analysis
346(1)
11.11 Multivariate Analysis of Variance and Other Multivariate Methods
346(2)
11.12 Structural Equation Modeling
348(1)
11.13 Multilevel Modeling
349(1)
11.14 Prediction Intervals
349(1)
11.15 Measures of Agreement
350(1)
11.16 Spatial Statistics
350(1)
11.17 Agricultural Applications
350(1)
11.18 Estimating the Number of Unseen Species
351(1)
11.19 Test Reliability
351(1)
11.20 Agreement Studies
351(1)
11.21 Genome-wide Association Studies
351(1)
11.22 National Security
352(1)
11.23 Miscellaneous
352(1)
11.24 Summary
353(10)
References
354(9)
Answers to Selected Exercises 363(6)
Index 369
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.
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