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E-raamat: Crossover Designs: Testing, Estimation, and Sample Size

(San Diego State University, USA)
  • Formaat: PDF+DRM
  • Sari: Statistics in Practice
  • Ilmumisaeg: 08-Aug-2016
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119114697
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Crossover Designs: Testing, Estimation, and Sample SizeSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Statistics in Practice
  • Ilmumisaeg: 08-Aug-2016
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119114697
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A comprehensive and practical resource for analyses of crossover designs

For ethical reasons, it is vital to keep the number of patients in a clinical trial as low as possible.  As evidenced by extensive research publications, crossover design can be a useful and powerful tool to reduce the number of patients needed for a parallel group design in studying treatments for non-curable chronic diseases.  

This book introduces commonly-used and well-established statistical tests and estimators in epidemiology that can easily be applied to hypothesis testing and estimation of the relative treatment effect for various types of data scale in crossover designs. Models with distribution-free random effects are assumed and hence most approaches considered here are semi-parametric. The book provides clinicians and biostatisticians with the exact test procedures and exact interval estimators, which are applicable even when the number of patients in a crossover trial is small.  Systematic discussion on sample size determination is also included, which will be a valuable resource for researchers involved in crossover trial design.

Key features:

  • Provides exact test procedures and interval estimators, which are especially of use in small-sample cases.
  • Presents most test procedures and interval estimators in closed-forms, enabling readers to calculate them by use of a pocket calculator or commonly-used statistical packages.
  • Each chapter is self-contained, allowing the book to be used a reference resource. 
  • Uses real-life examples to illustrate the practical use of test procedures and estimators
  • Provides extensive exercises to help readers appreciate the underlying theory, learn other relevant test procedures and understand how to calculate the required sample size. 

Crossover Designs: Testing, Estimation and Sample Size will be a useful resource for researchers from biostatistics, as well as pharmaceutical and clinical sciences.  It can also be used as a textbook or reference for graduate students studying clinical experiments.

About the author xi
Preface xii
About the companion website xiv
1 Crossover design -- definitions, notes, and limitations
1(6)
1.1 Unsuitability for acute or most infectious diseases
2(1)
1.2 Inappropriateness for treatments with long-lasting effects
2(1)
1.3 Loss of efficiency in the presence of carry-over effects
3(1)
1.4 Concerns of treatment-by-period interaction
3(1)
1.5 Flaw of the commonly used two-stage test procedure
4(1)
1.6 Higher risk of dropping out or being lost to follow-up
4(1)
1.7 More assumptions needed in use of a crossover design
5(1)
1.8 General principle and conditional approach used in the book
5(2)
2 AB/BA design in continuous data
7(23)
2.1 Testing non-equality of treatments
10(1)
2.2 Testing non-inferiority of an experimental treatment to an active control treatment
11(1)
2.3 Testing equivalence between an experimental treatment and an active control treatment
12(1)
2.4 Interval estimation of the mean difference
13(3)
2.5 Sample size determination
16(3)
2.5.1 Sample size for testing non-equality
16(1)
2.5.2 Sample size for testing non-inferiority
17(1)
2.5.3 Sample size for testing equivalence
18(1)
2.6 Hypothesis testing and estimation for the period effect
19(2)
2.7 Estimation of the relative treatment effect in the presence of differential carry-over effects
21(1)
2.8 Examples of SAS programs and results
22(8)
Exercises
27(3)
3 AB/BA design in dichotomous data
30(27)
3.1 Testing non-equality of treatments
34(2)
3.2 Testing non-inferiority of an experimental treatment to an active control treatment
36(3)
3.3 Testing equivalence between an experimental treatment and an active control treatment
39(1)
3.4 Interval estimation of the odds ratio
40(2)
3.5 Sample size determination
42(3)
3.5.1 Sample size for testing non-equality
42(1)
3.5.2 Sample size for testing non-inferiority
42(1)
3.5.3 Sample size for testing equivalence
43(2)
3.6 Hypothesis testing and estimation for the period effect
45(2)
3.7 Testing and estimation for carry-over effects
47(1)
3.8 SAS program codes and likelihood-based approach
48(9)
Exercises
51(6)
4 AB/BA design in ordinal data
57(18)
4.1 Testing non-equality of treatments
62(2)
4.2 Testing non-inferiority of an experimental treatment to an active control treatment
64(1)
4.3 Testing equivalence between an experimental treatment and an active control treatment
65(1)
4.4 Interval estimation of the generalized odds ratio
66(1)
4.5 Sample size determination
67(3)
4.5.1 Sample size for testing non-equality
67(1)
4.5.2 Sample size for testing non-inferiority
68(1)
4.5.3 Sample size for testing equivalence
68(2)
4.6 Hypothesis testing and estimation for the period effect
70(2)
4.7 SAS codes for the proportional odds model with normal random effects
72(3)
Exercises
74(1)
5 AB/BA design in frequency data
75(20)
5.1 Testing non-equality of treatments
78(3)
5.2 Testing non-inferiority of an experimental treatment to an active control treatment
81(2)
5.3 Testing equivalence between an experimental treatment and an active control treatment
83(1)
5.4 Interval estimation of the ratio of mean frequencies
84(2)
5.5 Sample size determination
86(2)
5.5.1 Sample size for testing non-equality
86(1)
5.5.2 Sample size for testing non-inferiority
87(1)
5.5.3 Sample size for testing equivalence
88(1)
5.6 Hypothesis testing and estimation for the period effect
88(2)
5.7 Estimation of the relative treatment effect in the presence of differential carry-over effects
90(5)
Exercises
92(3)
6 Three-treatment three-period crossover design in continuous data
95(20)
6.1 Testing non-equality between treatments and placebo
102(1)
6.2 Testing non-inferiority of an experimental treatment to an active control treatment
103(1)
6.3 Testing equivalence between an experimental treatment and an active control treatment
104(1)
6.4 Interval estimation of the mean difference
104(1)
6.5 Hypothesis testing and estimation for period effects
105(2)
6.6 Procedures for testing treatment-by-period interactions
107(2)
6.7 SAS program codes and results for constant variance
109(6)
Exercises
111(4)
7 Three-treatment three-period crossover design in dichotomous data
115(26)
7.1 Testing non-equality of treatments
121(5)
7.1.1 Asymptotic test procedures
121(2)
7.1.2 Exact test procedures
123(1)
7.1.3 Procedures for simultaneously testing non-equality of two experimental treatments versus a placebo
124(2)
7.2 Testing non-inferiority of an experimental treatment to an active control treatment
126(1)
7.3 Testing equivalence between an experimental treatment and an active control treatment
127(2)
7.4 Interval estimation of the odds ratio
129(2)
7.5 Hypothesis testing and estimation for period effects
131(2)
7.6 Procedures for testing treatment-by-period interactions
133(3)
7.7 SAS program codes and results for a logistic regression model with normal random effects
136(5)
Exercises
138(3)
8 Three-treatment three-period crossover design in ordinal data
141(23)
8.1 Testing non-equality of treatments
150(3)
8.1.1 Asymptotic test procedures
150(2)
8.1.2 Exact test procedure
152(1)
8.2 Testing non-inferiority of an experimental treatment to an active control treatment
153(1)
8.3 Testing equivalence between an experimental treatment and an active control treatment
153(1)
8.4 Interval estimation of the GOR
154(2)
8.5 Hypothesis testing and estimation for period effects
156(3)
8.6 Procedures for testing treatment-by-period interactions
159(1)
8.7 SAS program codes and results for the proportional odds model with normal random effects
160(4)
Exercises
162(2)
9 Three-treatment three-period crossover design in frequency data
164(19)
9.1 Testing non-equality between treatments and placebo
170(3)
9.2 Testing non-inferiority of an experimental treatment to an active control treatment
173(1)
9.3 Testing equivalence between an experimental treatment and an active control treatment
174(1)
9.4 Interval estimation of the ratio of mean frequencies
175(3)
9.5 Hypothesis testing and estimation for period effects
178(1)
9.6 Procedures for testing treatment-by-period interactions
179(4)
Exercises
181(2)
10 Three-treatment (incomplete block) crossover design in continuous and dichotomous data
183(25)
10.1 Continuous data
185(9)
10.1.1 Testing non-equality of treatments
188(1)
10.1.2 Testing non-equality between experimental treatments (or non-nullity of dose effects)
189(1)
10.1.3 Interval estimation of the mean difference
190(2)
10.1.4 SAS codes for fixed effects and mixed effects models
192(2)
10.2 Dichotomous data
194(14)
10.2.1 Testing non-equality of treatments
197(2)
10.2.2 Testing non-equality between experimental treatments (or non-nullity of dose effects)
199(1)
10.2.3 Testing non-inferiority of either experimental treatment to an active control treatment
199(1)
10.2.4 Interval estimation of the odds ratio
200(2)
10.2.5 SAS codes for the likelihood-based approach
202(1)
Exercises
203(5)
References 208(8)
Index 216
Kung-Jong Lui, Professor, Department of Mathematics and Statistics, San Diego State University, USA.
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