Sample size calculations for clustered and longitudinal outcomes in clinical research / Chul Ahn, Moonseoung Heo and Song Zhang.
Material type:
TextSeries: Chapman & Hall/CRC biostatistics seriesPublication details: Boca Raton : CRC Press, c2015.Description: xv, 244 p. : illustrations ; 24 cmISBN: - 9781466556263 (hardback)
- 000SB:610 23 Ah286
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SB:610 Ah286 (Browse shelf(Opens below)) | Available | 136412 |
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| 000SB:610.724 C552 Design and analysis of clinical trials : | 000SB:610.724 Ok41 Clinical trials with missing data : | 000SB:610.724 Z63 Applied missing data analysis in health sciences / | 000SB:610 Ah286 Sample size calculations for clustered and longitudinal outcomes in clinical research / | 000SB:610 Ai311 Statistical concepts and application in clinical medicine | 000SB:610 Al468 Practical statistics for medical research | 000SB:610 Al468 Practical statistics for medical research |
Includes bibliographical references and index.
1. Sample size determination for independent outcomes --
2. Sample size determination for clustered outcomes --
3. Sample size determination for repeated measurement outcomes using summary statistics --
4. Sample size determination for correlated outcome measurements using GEE --
5. Sample size determination for correlated outcomes from two-level randomized clinical trials --
6. Sample size determination for correlated outcomes from three-level randomized clinical trials--
Index.
The book focuses on issues specific to the two types of correlated outcomes: longitudinal and clustered. For clustered studies, the authors provide sample size formulas that accommodate variable cluster sizes and within-cluster correlation. For longitudinal studies, they present sample size formulas to account for within-subject correlation among repeated measurements and various missing data patterns. For multiple levels of clustering, the level at which to perform randomization actually becomes a design parameter. The authors show how this can greatly impact trial administration, analysis, and sample size requirement.
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