Crossover Designs

Testing, Estimation, and Sample Size

Inbunden, Engelska, 2016

1 349 kr

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A comprehensive and practical resource for analyses of crossover designsFor 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 estimatorsProvides 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.

Produktinformation

Utgivningsdatum2016-10-07
Mått152 x 231 x 15 mm
Vikt431 g
FormatInbunden
SpråkEngelska
SerieStatistics in Practice
Antal sidor248
FörlagJohn Wiley & Sons Inc
ISBN9781119114680

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About the author xiPreface xiiAbout the companion website xiv1 Crossover design – definitions, notes, and limitations 11.1 Unsuitability for acute or most infectious diseases 21.2 Inappropriateness for treatments with long-lasting effects 21.3 Loss of efficiency in the presence of carry-over effects 31.4 Concerns of treatment-by-period interaction 31.5 Flaw of the commonly used two-stage test procedure 41.6 Higher risk of dropping out or being lost to follow-up 41.7 More assumptions needed in use of a crossover design 51.8 General principle and conditional approach used in the book 52 AB/BA design in continuous data 72.1 Testing non-equality of treatments 102.2 Testing non-inferiority of an experimental treatment to an active control treatment 112.3 Testing equivalence between an experimental treatment and an active control treatment 122.4 Interval estimation of the mean difference 132.5 Sample size determination 162.5.1 Sample size for testing non-equality 162.5.2 Sample size for testing non-inferiority 172.5.3 Sample size for testing equivalence 182.6 Hypothesis testing and estimation for the period effect 192.7 Estimation of the relative treatment effect in the presence of differential carry-over effects 212.8 Examples of SAS programs and results 22Exercises 273 AB/BA design in dichotomous data 303.1 Testing non-equality of treatments 343.2 Testing non-inferiority of an experimental treatment to an active control treatment 363.3 Testing equivalence between an experimental treatment and an active control treatment 393.4 Interval estimation of the odds ratio 403.5 Sample size determination 423.5.1 Sample size for testing non-equality 423.5.2 Sample size for testing non-inferiority 423.5.3 Sample size for testing equivalence 433.6 Hypothesis testing and estimation for the period effect 453.7 Testing and estimation for carry-over effects 473.8 SAS program codes and likelihood-based approach 48Exercises 514 AB/BA design in ordinal data 574.1 Testing non-equality of treatments 624.2 Testing non-inferiority of an experimental treatment to an active control treatment 644.3 Testing equivalence between an experimental treatment and an active control treatment 654.4 Interval estimation of the generalized odds ratio 664.5 Sample size determination 674.5.1 Sample size for testing non-equality 674.5.2 Sample size for testing non-inferiority 684.5.3 Sample size for testing equivalence 684.6 Hypothesis testing and estimation for the period effect 704.7 SAS codes for the proportional odds model with normal random effects 72Exercises 745 AB/BA design in frequency data 755.1 Testing non-equality of treatments 785.2 Testing non-inferiority of an experimental treatment to an active control treatment 815.3 Testing equivalence between an experimental treatment and an active control treatment 835.4 Interval estimation of the ratio of mean frequencies 845.5 Sample size determination 865.5.1 Sample size for testing non-equality 865.5.2 Sample size for testing non-inferiority 875.5.3 Sample size for testing equivalence 885.6 Hypothesis testing and estimation for the period effect 885.7 Estimation of the relative treatment effect in the presence of differential carry-over effects 90Exercises 926 Three-treatment three-period crossover design in continuous data 956.1 Testing non-equality between treatments and placebo 1026.2 Testing non-inferiority of an experimental treatment to an active control treatment 1036.3 Testing equivalence between an experimental treatment and an active control treatment 1046.4 Interval estimation of the mean difference 1046.5 Hypothesis testing and estimation for period effects 1056.6 Procedures for testing treatment-by-period interactions 1076.7 SAS program codes and results for constant variance 109Exercises 1117 Three-treatment three-period crossover design in dichotomous data 1157.1 Testing non-equality of treatments 1217.1.1 Asymptotic test procedures 1217.1.2 Exact test procedures 1237.1.3 Procedures for simultaneously testing non-equality of two experimental treatments versus a placebo 1247.2 Testing non-inferiority of an experimental treatment to an active control treatment 1267.3 Testing equivalence between an experimental treatment and an active control treatment 1277.4 Interval estimation of the odds ratio 1297.5 Hypothesis testing and estimation for period effects 1317.6 Procedures for testing treatment-by-period interactions 1337.7 SAS program codes and results for a logistic regression model with normal random effects 136Exercises 1388 Three-treatment three-period crossover design in ordinal data 1418.1 Testing non-equality of treatments 1508.1.1 Asymptotic test procedures 1508.1.2 Exact test procedure 1528.2 Testing non-inferiority of an experimental treatment to an active control treatment 1538.3 Testing equivalence between an experimental treatment and an active control treatment 1538.4 Interval estimation of the GOR 1548.5 Hypothesis testing and estimation for period effects 1568.6 Procedures for testing treatment-by-period interactions 1598.7 SAS program codes and results for the proportional odds model with normal random effects 160Exercises 1629 Three-treatment three-period crossover design in frequency data 1649.1 Testing non-equality between treatments and placebo 1709.2 Testing non-inferiority of an experimental treatment to an active control treatment 1739.3 Testing equivalence between an experimental treatment and an active control treatment 1749.4 Interval estimation of the ratio of mean frequencies 1759.5 Hypothesis testing and estimation for period effects 1789.6 Procedures for testing treatment-by-period interactions 179Exercises 18110 Three-treatment (incomplete block) crossover design in continuous and dichotomous data 18310.1 Continuous data 18510.1.1 Testing non-equality of treatments 18810.1.2 Testing non-equality between experimental treatments (or non-nullity of dose effects) 18910.1.3 Interval estimation of the mean difference 19010.1.4 SAS codes for fixed effects and mixed effects models 19210.2 Dichotomous data 19410.2.1 Testing non-equality of treatments 19710.2.2 Testing non-equality between experimental treatments (or non-nullity of dose effects) 19910.2.3 Testing non-inferiority of either experimental treatment to an active control treatment 19910.2.4 Interval estimation of the odds ratio 20010.2.5 SAS codes for the likelihood-based approach 202Exercises 203References 208Index 216