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http://dx.doi.org/10.5351/CKSS.2008.15.3.353

Sample Size Calculations with Dropouts in Clinical Trials  

Lee, Ki-Hoon (School of Business, Jeonju University)
Publication Information
Communications for Statistical Applications and Methods / v.15, no.3, 2008 , pp. 353-365 More about this Journal
Abstract
The sample size in a clinical trial is determined by the hypothesis, the variance of observations, the effect size, the power and the significance level. Dropouts in clinical trials are inevitable, so we need to consider dropouts on the determination of sample size. It is common that some proportion corresponding to the expected dropout rate would be added to the sample size calculated from a mathematical equation. This paper proposes new equations for calculating sample size dealing with dropouts. Since we observe data longitudinally in most clinical trials, we can use a last observation to impute for missing one in the intention to treat (ITT) trials, and this technique is called last observation carried forward(LOCF). But LOCF might make deviations on the assumed variance and effect size, so that we could not guarantee the power of test with the sample size obtained from the existing equation. This study suggests the formulas for sample size involving information about dropouts and shows the properties of the proposed method in testing equality of means.
Keywords
Clinical trials; sample size; LOCF; ITT;
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1 Hoeing, J. M. and Heisey, D. M. (2001). The abuse of power: The pervasive fallacy of power calculations for data analysis, The American Statistician, 55, 19-24   DOI   ScienceOn
2 Kulinskaya, E., Staudte, R. G. and Gao, H. (2003). Power approximations in testing for unequal means in one-way ANOVA weighted for unequal variances, Communications in Statistics: Theory and Methods, 32, 2353-2371   DOI   ScienceOn
3 Spiegelhalter, D. J. and Freedman, L. S. (1986). A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion, Statistics in Medicine, 5, 1-13   DOI   ScienceOn
4 James, G. S. (1951). The comparison of several groups of observations when the ratios of the population variances are unknown, Biometrika, 38, 324-329   DOI
5 Welch, B. L. (1951). On the comparison of several mean values: An alternative approach, Biometrika, 38, 330-336   DOI
6 Lee, K. H. (2006). A study on one factorial longitudinal data analysis with informative drop-out, Journal of the Korean Data & Information Science Society, 17, 1053- 1065
7 Machin, D., Campbell, M., Fayers, P. and Pinol, A. (1997). Sample Size Tables for Clinical Studies, 2nd Edition, Blackwell Science, London, Edinburgh, Malden and Carlton
8 Moher, D., Dulberg, C. S. and Wells, G. A. (1994). Statistical power, sample size and their reporting in randomized controlled trials, The Journal of the American Medical Association, 272, 122-124   DOI
9 Altman, D. G., Machin, D., Bryant, T. N. and Gardner, M. J. (2000). Statistics with Confidence: Confidence Intervals and Statistical Guidelines, 2nd Edition, London: British Medical Journal
10 Altman, D. G. (1991). Practical Statistics for Medical Research, Chapman & Hall/CRC, London
11 Hedges, L. V. and Pigott, T. D. (2001). The power of statistical tests in meta-analysis, Psychological Methods, 6, 203-217   DOI   ScienceOn