Effect of dropouts on sample size estimates for test on trends across repeated measurements

Chul Ahn, Sin Ho Jung

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Sample size calculation is an important component at the design stage of clinical trials. We investigate the implications of dropouts for the sample size estimates in testing differences in the rates of changes produced by two treatments in a randomized parallel-groups repeated measurement design. Statistical models for calculating sample sizes for repeated measurement designs often fail to take into account the impact of dropouts correctly. In this article, we examine the impact of dropouts on sample size estimate and compare the power with the approach of Jung and Ahn / Jung, S. H., Ahn, C. (2003). Sample size estimation for GEE method for comparing slopes in repeated measurements data. Stat. Med. 22: 1305-1315/with that suggested by Patel and Rowe / Patel, H., Rowe, E. (1999). Sample size for comparing linear growth curves. J. Biopharm. Stat. 9:339-350/through a simulation study.

Original languageEnglish (US)
Pages (from-to)33-41
Number of pages9
JournalJournal of Biopharmaceutical Statistics
Volume15
Issue number1
DOIs
StatePublished - Jan 31 2005

Keywords

  • AR(I)
  • Compound symmetry
  • Independent working correlation
  • Monotone missing

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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