2007
DOI: 10.1016/j.jclinepi.2007.02.006
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A simple sample size formula for analysis of covariance in randomized clinical trials

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Cited by 450 publications
(405 citation statements)
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“…Differences in week 8 outcomes between the groups were assessed by analysis of covariance (ANCOVA), using the pretest results as the covariate, in a complete case analysis. ANCOVA has been recommended to increase the precision of estimates and the statistical power while also reducing the sample size requirements [25][26]. As stated by White et al, complete case analysis is appropriate for randomized trials with pretest-posttest designs [27].…”
Section: Discussionmentioning
confidence: 99%
“…Differences in week 8 outcomes between the groups were assessed by analysis of covariance (ANCOVA), using the pretest results as the covariate, in a complete case analysis. ANCOVA has been recommended to increase the precision of estimates and the statistical power while also reducing the sample size requirements [25][26]. As stated by White et al, complete case analysis is appropriate for randomized trials with pretest-posttest designs [27].…”
Section: Discussionmentioning
confidence: 99%
“…Using an a = 0.05 level of significance and including specimens from both regions of the femur, the sample size (n = 12 smallest sample size) had a power of 0.99 to detect a difference in yield strain of 1.72*10 À3 strain (20% of the expected mean value) with analysis of variance. Using the approximation of statistical power for analysis of covariance proposed by Borm and colleagues [9], the smallest sample size had a power of 0.99 to observe a 20% decrease in the relationship between bone volume fraction and either Young's modulus or yield strength. Proximal femur specimens had an average height to diameter ratio of approximately 2.3:1 (length 20.73 ± 2.67 mm; diameter 8.15 ± 0.06 mm, mean ± SD); distal femur specimens had an average height to diameter ratio of approximately 2.5:1 (length 18.87 ± 1.40 mm, diameter 8.24 ± 0.03 mm).…”
Section: Methodsmentioning
confidence: 99%
“…This type of analysis accounts for regression to the mean and raises power of the analyses (Borm et al, 2007;Kenward and Roger, 1997).…”
Section: Statistical Analysesmentioning
confidence: 99%