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Crossover designs : testing, estimation, and sample size / Kung-Jong Lui.

By: Material type: TextTextSeries: Statistics in practicePublication details: Chichester : John Wiley, 2016.Description: xiii, 227 pages : illustrations ; 24 cmISBN:
  • 9781119114680
Subject(s): Additional physical formats: Online version:: Crossover designsDDC classification:
  • 000SB:610 23 L952
Contents:
1. Crossover design : definitions, notes and limitations -- 2. AB/BA design in continuous data -- 3. AB/BA design in dichotomous data -- 4. AB/BA design in ordinal data -- 5. AB/BA design in frequency data -- 6. Three-treatment three-period crossover design in continuous data -- 7. Three-treatment three-period crossover design in dichotomous data -- 8. Three-treatment three-period crossover design in ordinal data -- 9. Three-treatment three-period crossover design in frequency data -- 10. Three-treatment (incomplete block) crossover design in continuous and dichotomous data.
Summary: 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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SB:610 L952 (Browse shelf(Opens below)) Available 137741
Total holds: 0

Includes bibliographical references and index.

1. Crossover design : definitions, notes and limitations --
2. AB/BA design in continuous data --
3. AB/BA design in dichotomous data --
4. AB/BA design in ordinal data --
5. AB/BA design in frequency data --
6. Three-treatment three-period crossover design in continuous data --
7. Three-treatment three-period crossover design in dichotomous data --
8. Three-treatment three-period crossover design in ordinal data --
9. Three-treatment three-period crossover design in frequency data --
10. Three-treatment (incomplete block) crossover design in continuous and dichotomous data.

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.

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