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Sample Size Estimation for Repeated Measures Analysis in Randomized Clinical Trials with Missing Data

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  • Lu Kaifeng

    (Merck & Co.)

  • Luo Xiaohui

    (Merck & Co.)

  • Chen Pei-Yun

    (Merck & Co.)

Abstract

In designing longitudinal studies, researchers must determine the number of subjects to randomize based on the power to detect a clinically meaningful treatment difference and a proposed analysis plan. In this paper, we present formulas for sample size estimation and an assessment of statistical power for a two-treatment repeated measures design allowing for subject attrition. These formulas can be used for comparing two treatment groups across time in terms of linear contrasts. Subjects are assumed to drop out of the study at random so that the missing data do not alter the parameters of interest.

Suggested Citation

  • Lu Kaifeng & Luo Xiaohui & Chen Pei-Yun, 2008. "Sample Size Estimation for Repeated Measures Analysis in Randomized Clinical Trials with Missing Data," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-16, June.
  • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:9
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    References listed on IDEAS

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    1. Wang, Xiaogang & Qiu, Weiliang & Zamar, Ruben H., 2007. "CLUES: A non-parametric clustering method based on local shrinking," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 286-298, September.
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