Atnaujinkite slapukų nuostatas

El. knyga: Sample Size Calculations in Clinical Research

5.00/5 (2 ratings by Goodreads)
(Duke Univ, USA), (Duke University School of Medicine, Durham, NC, USA), (Department of Statistics, University of Wisconsin, USA),
Kitos knygos pagal šią temą:
Sample Size Calculations in Clinical ResearchDidesnis paveikslėlis
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

Praise for the Second Edition:

" this is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study." -Biometrics

"This impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered " Journal of the Royal Statistical Society

Sample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, this book includes a well-balanced summary of current and emerging clinical issues, regulatory requirements, and recently developed statistical methodologies for sample size calculation.

Features:











Compares the relative merits and disadvantages of statistical methods for sample size calculations





Explains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stages





Presents real-world examples from several therapeutic areas, including cardiovascular medicine, the central nervous system, anti-infective medicine, oncology, and womens health





Provides sample size calculations for dose response studies, microarray studies, and Bayesian approaches

This new edition is updated throughout, includes many new sections, and five new chapters on emerging topics: two stage seamless adaptive designs, cluster randomized trial design, zero-inflated Poisson distribution, clinical trials with extremely low incidence rates, and clinical trial simulation.

Recenzijos

Praise for the Second Edition:

" this is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study." -Biometrics

"This impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered " Journal of the Royal Statistical Society

"The book is nicely set out with an introduction to the basic idea of each topic, followed by various formulae that lead to power calculations . . . In all, I consider this book to be well written, and it touches on quite a number of more recent topics in sample size determination. Consequently, it will be a useful addition to clinical statisticians as a point of reference to solve more complex issues in power calculations during the design of a clinical trial." Steve Su, International Society for Clinical Biostatistics

Sample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, this book includes a well-balanced summary of current and emerging clinical issues, regulatory requirements, and recently developed statistical methodologies for sample size calculation.

Features:











Compares the relative merits and disadvantages of statistical methods for sample size calculations





Explains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stages





Presents real-world examples from several therapeutic areas, including cardiovascular medicine, the central nervous system, anti-infective medicine, oncology, and womens health





Provides sample size calculations for dose response studies, microarray studies, and Bayesian approaches

This new edition is updated throughout, includes many new sections, and five new chapters on emerging topics: two stage seamless adaptive designs, cluster randomized trial design, zero-inflated Poisson distribution, clinical trials with extremely low incidence rates, and clinical trial simulation. Praise for the Second Edition:

" this is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study." -Biometrics

"This impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered " Journal of the Royal Statistical Society"The book is nicely set out with an introduction to the basic idea of each topic, followed by various formulae that lead to power calculations . . . In all, I consider this book to be well written, and it touches on quite a number of more recent topics in sample size determination. Consequently, it will be a useful addition to clinical statisticians as a point of reference to solve more complex issues in power calculations during the design of a clinical trial." Steve Su, International Society for Clinical Biostatistics

Sample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, this book includes a well-balanced summary of current and emerging clinical issues, regulatory requirements, and recently developed statistical methodologies for sample size calculation.

Features:











Compares the relative merits and disadvantages of statistical methods for sample size calculations





Explains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stages





Presents real-world examples from several therapeutic areas, including cardiovascular medicine, the central nervous system, anti-infective medicine, oncology, and womens health





Provides sample size calculations for dose response studies, microarray studies, and Bayesian approaches

This new edition is updated throughout, includes many new sections, and five new chapters on emerging topics: two stage seamless adaptive designs, cluster randomized trial design, zero-inflated Poisson distribution, clinical trials with extremely low incidence rates, and clinical trial simulation.

Preface xvii
1 Introduction 1(20)
1.1 Regulatory Requirement
2(3)
1.1.1 Adequate and Well-Controlled Clinical Trials
2(1)
1.1.2 Substantial Evidence
3(1)
1.1.3 Why At Least Two Studies?
3(1)
1.1.4 Substantial Evidence with a Single Trial
4(1)
1.1.5 Sample Size
5(1)
1.2 Basic Considerations
5(6)
1.2.1 Study Objectives
6(1)
1.2.2 Study Design
6(1)
1.2.3 Hypotheses
7(2)
1.2.3.1 Test for Equality
7(1)
1.2.3.2 Test for Noninferiority
8(1)
1.2.3.3 Test for Superiority
8(1)
1.2.3.4 Test for Equivalence
8(1)
1.2.3.5 Relationship among Noninferiority, Superiority, and Equivalence
9(1)
1.2.4 Primary Study Endpoint
9(1)
1.2.5 Clinically Meaningful Difference
10(1)
1.3 Procedures for Sample Size Calculation
11(7)
1.3.1 Type I and Type II Errors
11(1)
1.3.2 Precision Analysis
12(1)
1.3.3 Power Analysis
13(2)
1.3.4 Probability Assessment
15(1)
1.3.5 Reproducibility Probability
16(2)
1.3.6 Sample Size Reestimation without Unblinding
18(1)
1.4 Aims and Structure of this Book
18(3)
1.4.1 Aim of this Book
18(1)
1.4.2 Structure of this Book
19(2)
2 Considerations Prior to Sample Size Calculation 21(18)
2.1 Confounding and Interaction
21(2)
2.1.1 Confounding
21(1)
2.1.2 Interaction
22(1)
2.1.3 Remark
22(1)
2.2 One-Sided Test versus Two-Sided Test
23(1)
2.2.1 Remark
24(1)
2.3 Crossover Design versus Parallel Design
24(2)
2.3.1 Intersubject and Intrasubject Variabilities
25(1)
2.3.2 Crossover Design
25(1)
2.3.3 Parallel Design
26(1)
2.3.4 Remark
26(1)
2.4 Subgroup/Interim Analyses
26(3)
2.4.1 Group Sequential Boundaries
27(1)
2.4.2 Alpha Spending Function
28(1)
2.5 Data Transformation
29(2)
2.5.1 Remark
31(1)
2.6 Practical Issues
31(8)
2.6.1 Unequal Treatment Allocation
31(1)
2.6.2 Adjustment for Dropouts or Covariates
32(1)
2.6.3 Mixed-Up Randomization Schedules
33(2)
2.6.4 Treatment or Center Imbalance
35(2)
2.6.5 Multiplicity
37(1)
2.6.6 Multiple-Stage Design for Early Stopping
37(1)
2.6.7 Rare Incidence Rate
38(1)
3 Comparing Means 39(32)
3.1 One-Sample Design
39(8)
3.1.1 Test for Equality
40(2)
3.1.2 Test for Noninferiority/Superiority
42(2)
3.1.3 Test for Equivalence
44(1)
3.1.4 An Example
45(2)
3.1.4.1 Test for Equality
45(1)
3.1.4.2 Test for Noninferiority
46(1)
3.1.4.3 Test for Equivalence
46(1)
3.2 Two-Sample Parallel Design
47(7)
3.2.1 Test for Equality
47(3)
3.2.2 Test for Noninferority/Superiority
50(1)
3.2.3 Test for Equivalence
51(1)
3.2.4 An Example
52(2)
3.2.4.1 Test for Equality
53(1)
3.2.4.2 Test for Noninferiority
53(1)
3.2.4.3 Test for Equivalence
54(1)
3.2.5 Remarks
54(1)
3.3 Two-Sample Crossover Design
54(5)
3.3.1 Test for Equality
55(1)
3.3.2 Test for Noninferiority/Superiority
56(1)
3.3.3 Test for Equivalence
57(1)
3.3.4 An Example
58(1)
3.3.4.1 Therapeutic Equivalence
58(1)
3.3.4.2 Noninferiority
58(1)
3.3.5 Remarks
59(1)
3.4 Multiple-Sample One-Way ANOVA
59(4)
3.4.1 Pairwise Comparison
60(1)
3.4.2 Simultaneous Comparison
61(1)
3.4.3 An Example
61(1)
3.4.4 Remarks
62(1)
3.5 Multiple-Sample Williams Design
63(3)
3.5.1 Test for Equality
64(1)
3.5.2 Test for Noninferiority/Superiority
65(1)
3.5.3 Test for Equivalence
65(1)
3.5.4 An Example
66(1)
3.6 Practical Issues
66(5)
3.6.1 One-Sided versus Two-Sided Test
67(1)
3.6.2 Parallel Design versus Crossover Design
67(1)
3.6.3 Sensitivity Analysis
68(3)
4 Large Sample Tests for Proportions 71(32)
4.1 One-Sample Design
71(5)
4.1.1 Test for Equality
72(1)
4.1.2 Test for Noninferiority/Superiority
73(1)
4.1.3 Test for Equivalence
74(1)
4.1.4 An Example
74(1)
4.1.4.1 Test for Equality
75(1)
4.1.4.2 Test for Noninferiority
75(1)
4.1.4.3 Test for Equivalence
75(1)
4.1.5 Remarks
75(1)
4.2 Two-Sample Parallel Design
76(6)
4.2.1 Test for Equality
77(1)
4.2.2 Test for Noninferiority/Superiority
77(1)
4.2.3 Test for Equivalence
78(1)
4.2.4 An Example
79(2)
4.2.4.1 Test for Equality
80(1)
4.2.4.2 Test for Noninferiority
80(1)
4.2.4.3 Test for Superiority
80(1)
4.2.4.4 Test for Equivalence
81(1)
4.2.5 Remarks
81(1)
4.3 Two-Sample Crossover Design
82(4)
4.3.1 Test for Equality
83(1)
4.3.2 Test for Noninferiority/Superiority
84(1)
4.3.3 Test for Equivalence
84(1)
4.3.4 An Example
85(1)
4.3.4.1 Test for Equality
85(1)
4.3.4.2 Test for Noninferiority
86(1)
4.3.4.3 Test for Equivalence
86(1)
4.3.5 Remarks
86(1)
4.4 One-Way Analysis of Variance
86(2)
4.4.1 Pairwise Comparison
87(1)
4.4.2 An Example
87(1)
4.4.3 Remarks
88(1)
4.5 Williams Design
88(4)
4.5.1 Test for Equality
89(1)
4.5.2 Test for Noninferiority/Superiority
89(1)
4.5.3 Test for Equivalence
90(1)
4.5.4 An Example
91(1)
4.5.4.1 Test for Equality
91(1)
4.5.4.2 Test for Superiority
92(1)
4.5.4.3 Test for Equivalence
92(1)
4.6 Relative Risk-Parallel Design
92(4)
4.6.1 Test for Equality
93(1)
4.6.2 Test for Noninferiority/Superiority
94(1)
4.6.3 Test for Equivalence
94(1)
4.6.4 An Example
95(1)
4.6.4.1 Test for Equality
95(1)
4.6.4.2 Test for Superiority
96(1)
4.6.4.3 Test for Equivalence
96(1)
4.7 Relative Risk-Crossover Design
96(3)
4.7.1 Test for Equality
97(1)
4.7.2 Test for Noninferiority/Superiority
97(1)
4.7.3 Test for Equivalence
98(1)
4.8 Practical Issues
99(4)
4.8.1 Exact and Asymptotic Tests
99(1)
4.8.2 Variance Estimates
99(2)
4.8.3 Stratified Analysis
101(1)
4.8.4 Equivalence Test for More Than Two Proportions
102(1)
5 Exact Tests for Proportions 103(28)
5.1 Binomial Test
103(2)
5.1.1 The Procedure
103(1)
5.1.2 Remarks
104(1)
5.1.3 An Example
104(1)
5.2 Negative Binomial
105(3)
5.2.1 Negative Binomial Distribution
106(1)
5.2.2 Sample Size Requirement
107(1)
5.3 Fisher's Exact Test
108(3)
5.3.1 The Procedure
109(1)
5.3.2 Remarks
109(1)
5.3.3 An Example
109(2)
5.4 Optimal Multiple-Stage Designs for Single-Arm Trials
111(11)
5.4.1 Optimal Two-Stage Designs
111(2)
5.4.2 Flexible Two-Stage Designs
113(1)
5.4.3 Optimal Three-Stage Designs
114(8)
5.5 Flexible Designs for Multiple-Arm Trials
122(7)
5.6 Remarks
129(2)
6 Tests for Goodness-of-Fit and Contingency Tables 131(16)
6.1 Tests for Goodness-of-Fit
131(2)
6.1.1 Pearson's Test
131(1)
6.1.2 An Example
132(1)
6.2 Test for Independence: Single Stratum
133(3)
6.2.1 Pearson's Test
134(1)
6.2.2 Likelihood Ratio Test
135(1)
6.2.3 An Example
136(1)
6.3 Test for Independence: Multiple Strata
136(2)
6.3.1 Cochran-Mantel-Haenszel Test
137(1)
6.3.2 An Example
138(1)
6.4 Test for Categorical Shift
138(5)
6.4.1 McNemar's Test
139(2)
6.4.2 Stuart-Maxwell Test
141(1)
6.4.3 Examples
142(1)
6.4.3.1 McNemar's Test
142(1)
6.4.3.2 Stuart-Maxwell Test
142(1)
6.5 Carryover Effect Test
143(2)
6.5.1 Test Procedure
143(2)
6.5.2 An Example
145(1)
6.6 Practical Issues
145(2)
6.6.1 Local Alternative versus Fixed Alternative
145(1)
6.6.2 Random versus Fixed Marginal Total
146(1)
6.6.3 r x c versus p x r x c
146(1)
7 Comparing Time-to-Event Data 147(22)
7.1 Basic Concepts
147(3)
7.1.1 Survival Function
148(1)
7.1.2 Median Survival Time
148(1)
7.1.3 Hazard Function
148(1)
7.1.4 An Example
149(1)
7.2 Exponential Model
150(8)
7.2.1 Test for Equality
152(1)
7.2.2 Test for Noninferiority/Superiority
153(1)
7.2.3 Test for Equivalence
154(1)
7.2.4 An Example
155(1)
7.2.4.1 Test for Equality
155(1)
7.2.4.2 Test for Superiority
156(1)
7.2.4.3 Test for Equivalence
156(1)
7.2.5 Remarks
156(2)
7.2.5.1 Unconditional versus Conditional
156(1)
7.2.5.2 Losses to Follow-Up, Dropout, and Noncompliance
157(1)
7.3 Cox's Proportional Hazards Model
158(5)
7.3.1 Test for Equality
159(2)
7.3.2 Test for Noninferiority/Superiority
161(1)
7.3.3 Test for Equivalence
162(1)
7.3.4 An Example
162(1)
7.3.4.1 Test for Equality
162(1)
7.3.4.2 Test for Superiority
163(1)
7.3.4.3 Test for Equivalence
163(1)
7.4 Weighted Log-Rank Test
163(5)
7.4.1 Tarone-Ware Test
163(2)
7.4.2 An Example
165(3)
7.5 Practical Issues
168(1)
7.5.1 Binomial versus Time to Event
168(1)
7.5.2 Local Alternative versus Fixed Alternative
168(1)
7.5.3 One-Sample versus Historical Control
168(1)
8 Group Sequential Methods 169(22)
8.1 Pocock's Test
169(3)
8.1.1 The Procedure
169(2)
8.1.2 An Example
171(1)
8.2 O'Brien and Fleming's Test
172(3)
8.2.1 The Procedure
173(1)
8.2.2 An Example
174(1)
8.3 Wang and Tsiatis' Test
175(2)
8.3.1 The Procedure
175(1)
8.3.2 An Example
175(2)
8.4 Inner Wedge Test
177(3)
8.4.1 The Procedure
177(1)
8.4.2 An Example
178(2)
8.5 Binary Variables
180(1)
8.5.1 The Procedure
180(1)
8.5.2 An Example
180(1)
8.6 Time-to-Event Data
181(2)
8.6.1 The Procedure
181(1)
8.6.2 An Example
182(1)
8.7 Alpha-Spending Function
183(2)
8.8 Sample Size Reestimation
185(2)
8.8.1 The Procedure
185(1)
8.8.2 An Example
186(1)
8.9 Conditional Power
187(2)
8.9.1 Comparing Means
187(1)
8.9.2 Comparing Proportions
188(1)
8.10 Practical Issues
189(2)
9 Comparing Variabilities 191(42)
9.1 Comparing Intrasubject Variabilities
191(9)
9.1.1 Parallel Design with Replicates
192(3)
9.1.1.1 Test for Equality
192(1)
9.1.1.2 Test for Noninferiority/Superiority
193(1)
9.1.1.3 Test for Similarity
194(1)
9.1.1.4 An Example
195(1)
9.1.2 Replicated Crossover Design
195(5)
9.1.2.1 Test for Equality
197(1)
9.1.2.2 Test for Noninferiority/Superiority
198(1)
9.1.2.3 Test for Similarity
198(1)
9.1.2.4 An Example
199(1)
9.2 Comparing Intrasubject CVs
200(9)
9.2.1 Simple Random Effects Model
200(4)
9.2.1.1 Test for Equality
201(1)
9.2.1.2 Test for Noninferiority/Superiority
202(1)
9.2.1.3 Test for Similarity
203(1)
9.2.1.4 An Example
203(1)
9.2.2 Conditional Random Effects Model
204(5)
9.2.2.1 Test for Equality
206(1)
9.2.2.2 Test for Noninferiority/Superiority
206(1)
9.2.2.3 Test for Similarity
207(1)
9.2.2.4 An Example
208(1)
9.2.2.5 Remarks
208(1)
9.3 Comparing Intersubject Variabilities
209(8)
9.3.1 Parallel Design with Replicates
209(4)
9.3.1.1 Test for Equality
210(1)
9.3.1.2 Test for Noninferiority/Superiority
211(1)
9.3.1.3 An Example
212(1)
9.3.2 Replicated Crossover Design
213(4)
9.3.2.1 Test for Equality
213(2)
9.3.2.2 Test for Noninferiority/Superiority
215(1)
9.3.2.3 An Example
216(1)
9.4 Comparing Total Variabilities
217(14)
9.4.1 Parallel Designs without Replicates
217(4)
9.4.1.1 Test for Equality
218(1)
9.4.1.2 Test for Noninferiority/Superiority
219(1)
9.4.1.3 Test for Similarity
219(1)
9.4.1.4 An Example
220(1)
9.4.2 Parallel Design with Replicates
221(3)
9.4.2.1 Test for Equality
221(1)
9.4.2.2 Test for Noninferiority/Superiority
222(1)
9.4.2.3 An Example
223(1)
9.4.3 The Standard 2 x 2 Crossover Design
224(3)
9.4.3.1 Test for Equality
224(2)
9.4.3.2 Test for Noninferiority/Superiority
226(1)
9.4.3.3 An Example
227(1)
9.4.4 Replicated 2 x 2m Crossover Design
227(8)
9.4.4.1 Test for Equality
227(2)
9.4.4.2 Test for Noninferiority/Superiority
229(1)
9.4.4.3 An Example
230(1)
9.5 Practical Issues
231(2)
10 Bioequivalence Testing 233(24)
10.1 Bioequivalence Criteria
234(1)
10.2 Average Bioequivalence
235(3)
10.2.1 An Example
237(1)
10.3 Population Bioequivalence
238(4)
10.3.1 An Example
240(2)
10.4 Individual Bioequivalence
242(6)
10.4.1 An Example
246(2)
10.5 In Vitro Bioequivalence
248(5)
10.5.1 An Example
252(1)
10.6 Sample Size Requirement for Analytical Similarity Assessment of Biosimilar Products
253(4)
10.6.1 FDA's Tiered Approach
253(1)
10.6.2 Sample Size Requirement
253(4)
11 Dose-Response Studies 257(20)
11.1 Continuous Response
257(4)
11.1.1 Linear Contrast Test
258(3)
11.2 Binary Response
261(1)
11.3 Time-to-Event Endpoint
262(2)
11.4 Williams' Test for Minimum Effective Dose
264(4)
11.5 Cochran-Armitage's Test for Trend
268(3)
11.6 Dose Escalation Trials
271(5)
11.6.1 A + B Escalation Design without Dose De-Escalation
272(2)
11.6.2 A + B Escalation Design with Dose De-Escalation
274(2)
11.7 Concluding Remarks
276(1)
12 Microarray Studies 277(20)
12.1 Literature Review
277(1)
12.2 FDR Control
278(10)
12.2.1 Model and Assumptions
278(2)
12.2.2 Sample Size Calculation
280(8)
12.3 FWER Control
288(7)
12.3.1 Multiple Testing Procedures
288(2)
12.3.2 Sample Size Calculation
290(3)
12.3.3 Leukemia Example
293(2)
12.4 Concluding Remarks
295(2)
13 Bayesian Sample Size Calculation 297(24)
13.1 Posterior Credible Interval Approach
298(14)
13.1.1 Three Selection Criteria
298(2)
13.1.1.1 Average Coverage Criterion
299(1)
13.1.1.2 Average Length Criterion
299(1)
13.1.1.3 Worst Outcome Criterion
300(1)
13.1.2 One Sample
300(5)
13.1.2.1 Known Precision
300(1)
13.1.2.2 Unknown Precision
301(1)
13.1.2.3 Mixed Bayesian-Likelihood
302(3)
13.1.3 Two-Sample with Common Precision
305(3)
13.1.3.1 Known Common Precision
306(1)
13.1.3.2 Unknown Common Precision
307(1)
13.1.4 Two-Sample with Unequal Precisions
308(4)
13.1.4.1 Known Precision
310(1)
13.1.4.2 Unknown Precisions
311(1)
13.2 Posterior Error Approach
312(4)
13.2.1 Posterior Error Rate
312(2)
13.2.2 Comparing Means
314(2)
13.3 Bootstrap-Median Approach
316(3)
13.3.1 Background
317(1)
13.3.2 Bootstrap-Median Approach
318(1)
13.4 Concluding Remarks
319(2)
14 Nonparametrics 321(16)
14.1 Violation of Assumptions
321(2)
14.2 One-Sample Location Problem
323(4)
14.2.1 Remark
326(1)
14.2.2 An Example
326(1)
14.3 Two-Sample Location Problem
327(3)
14.3.1 Remark
329(1)
14.3.2 An Example
330(1)
14.4 Test for Independence
330(4)
14.4.1 An Example
333(1)
14.5 Practical Issues
334(3)
14.5.1 Bootstrapping
334(1)
14.5.2 Comparing Variabilities
334(1)
14.5.3 Multiple-Sample Location Problem
334(1)
14.5.4 Testing Scale Parameters
335(2)
15 Sample Size Calculations for Cluster Randomized Trials 337(12)
15.1 Unmatched Trials
338(4)
15.1.1 Comparison of Means
339(1)
15.1.2 Comparison of Proportions
340(1)
15.1.3 Comparison of Incidence Rates
341(1)
15.1.4 Further Remarks
342(1)
15.2 Matched Trials
342(5)
15.2.1 Comparison of Means
343(1)
15.2.2 Comparison of Proportions
344(1)
15.2.3 Comparison of Incidence Rates
345(2)
15.3 Stratified Trials
347(2)
16 Test for Homogeneity of Two Zero-Inflated Poisson Population 349(24)
16.1 Zero-Inflated Poisson Distribution
350(1)
16.2 Testing Differences between Treatment Groups
351(5)
16.2.1 Testing the Difference in Both Groups of Zeros and Nonzeros
352(2)
16.2.2 Testing the Difference in the Groups of Zeros
354(1)
16.2.3 Testing the Difference of the Groups of Nonzeros
355(1)
16.3 Sample Size Calculation
356(7)
16.3.1 Testing the Difference in the Groups of Both Zeros and Nonzeros
356(1)
16.3.2 Testing the Difference in the Groups of Zeros between Treatments
357(1)
16.3.3 Testing the Difference in the Groups of Nonzeros between Treatments
357(4)
16.3.4 An Example
361(2)
16.4 Multivariate ZIP
363(7)
16.4.1 Bivariate ZIP
363(3)
16.4.2 Comparing the Effects of Control and Test Treatment
366(1)
16.4.3 Sample Size Calculation
367(1)
16.4.4 An Example
368(2)
16.5 Concluding Remarks
370(1)
Appendix
371(2)
17 Sample Size for Clinical Trials with Extremely Low Incidence Rate 373(16)
17.1 Clinical Studies with Extremely Low Incidence Rate
374(1)
17.2 Classical Methods for Sample Size Determination
374(4)
17.2.1 Power Analysis
374(1)
17.2.2 Precision Analysis
375(1)
17.2.3 Remarks
376(2)
17.3 Chow and Chiu's Procedure for Sample Size Estimation
378(2)
17.3.1 Basic Idea of Chow and Chiu's Procedure
378(1)
17.3.2 Sensitivity Analysis
379(1)
17.3.3 An Example
380(1)
17.4 Data Safety Monitoring Procedure
380(3)
17.5 Concluding Remarks
383(6)
18 Sample Size Calculation for Two-Stage Adaptive Trial Design 389(32)
18.1 Types of Two-Stage Adaptive Designs
390(1)
18.2 Analysis and Sample Size for Category SS Adaptive Designs
391(7)
18.2.1 Theoretical Framework
392(2)
18.2.2 Two-Stage Design
394(3)
18.2.3 Conditional Power
397(1)
18.3 Analysis and Sample Size for Category II SD Adaptive Designs
398(16)
18.3.1 Continuous Endpoints
398(4)
18.3.2 Binary Responses
402(3)
18.3.3 Time-to-Event Endpoints
405(9)
18.4 Analysis and Sample Size for Category III DS and IV DD Two-Stage Adaptive Designs
414(5)
18.4.1 Nonadaptive Version
415(1)
18.4.2 Adaptive Version
416(1)
18.4.3 A Case Study of Hepatitis C Virus Infection
417(2)
18.5 Concluding Remarks
419(2)
19 Simulation-Based Sample Size and Power Analysis 421(6)
19.1 Example: Survival Study with Nonconstant Treatment Effect
422(2)
19.2 Example: Cluster Randomized Study with Stepped Wedge Design
424(3)
20 Sample Size Calculation in Other Areas 427(38)
20.1 QT/QTc Studies with Time-Dependent Replicates
427(8)
20.1.1 Study Designs and Models
428(1)
20.1.2 Power and Sample Size Calculation
429(4)
20.1.3 Extension
433(1)
20.1.4 Remarks
434(1)
20.2 Propensity Analysis in Nonrandomized Studies
435(5)
20.2.1 Weighted Mantel-Haenszel Test
435(1)
20.2.2 Power and Sample Size
436(3)
20.2.3 Simulations
439(1)
20.2.4 Concluding Remarks
440(1)
20.3 ANOVA with Repeated Measures
440(5)
20.3.1 Statistical Model
440(2)
20.3.2 Hypotheses Testing
442(1)
20.3.3 Sample Size Calculation
443(1)
20.3.4 An Example
443(2)
20.4 Quality of Life
445(4)
20.4.1 Time Series Model
446(2)
20.4.2 Sample Size Calculation
448(1)
20.4.3 An Example
448(1)
20.5 Bridging Studies
449(8)
20.5.1 Sensitivity Index
449(3)
20.5.2 Assessment of Similarity
452(5)
20.5.3 Remarks
457(1)
20.6 Vaccine Clinical Trials
457(8)
20.6.1 Reduction in Disease Incidence
458(1)
20.6.2 Evaluation of Vaccine Efficacy with Extremely Low Disease Incidence
459(2)
20.6.3 Relative Vaccine Efficacy
461(1)
20.6.4 Composite Efficacy Measure
461(2)
20.6.5 Remarks
463(2)
Bibliography 465(16)
Index 481
Shein-Chung Chow, PhD, is a professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine. Dr. Chow is also an adjunct professor at Duke-National University of Singapore Graduate Medical School, an adjunct professor at North Carolina State University, and founding director of the Global Clinical Trial and Research Center in Tianjin, China. He is editor-in-chief of the Journal of Biopharmaceutical Statistics and editor-in-chief of the Chapman & Hall/CRC Biostatistics Series. He is the author or co-author of more than 250 papers and 24 books, including Adaptive Design Methods in Clinical Trials, Second Edition, Handbook of Adaptive Designs in Pharmaceutical and Clinical Development, and Controversial Statistical Issues in Clinical Trials. A fellow of the ASA and member of the ISI, Dr. Chow has received the ASA Chapter Service Recognition Award, the DIA Outstanding Service Award, and the ICSA Extraordinary Achievement Award.

Dr. Lokhnygina is an Assistant Professor of Biostatistics and Bioinformatics at Duke University and a faculty member at Duke Clinical Research Institute. Her primary research interests are in statistical methods for multicenter clinical trials, particularly in application to cardiovascular and diabetes research.
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97813517271292e.html
Raktažodžiai: