A simple sample size formula for analysis of covariance in randomized clinical trials
A simple sample size formula for analysis of covariance in randomized clinical trials
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DOI：
10.1016/j.jclinepi.2007.02.006
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发表时间：
2007-12-01
影响因子：
7.2
通讯作者：
Lemmens, Wim A. J. G.
中科院分区：
文献类型：
--
作者：
Borm, George F.;Fransen, Jaap;Lemmens, Wim A. J. G.
目的：可以使用协方差分析(ANCOVA)或t检验方法来分析比较两种治疗方法在连续结果上的随机临床试验。研究设计与设置：我们推导了一个近似的样本量公式。模拟用于验证公式的准确性，并改进小规模试验的近似值。结果：如果基线和随访时测量的结果之间的相关性是Rho，则(1-Rho(2))n个受试者的ANCOVA比较组与n个受试者的t检验对照组具有相同的能力。对于相同的数据，用ANCOVA代替t检验，提高了治疗估计的精确度，可信区间的长度减少了因子根1-Rho(2)。结论：ANCOVA可以显著减少试验所需的患者数量。(C)2007 Elsevier Inc.保留所有权利。
Objective: Randomized clinical trials that compare two treatments on a continuous outcome can be analyzed using analysis of covariance (ANCOVA) or a t-test approach. We present a method for the sample size calculation when ANCOVA is used.Study Design and Setting: We derived an approximate sample size formula. Simulations were used to verify the accuracy of the formula and to improve the approximation for small trials. The sample size calculations are illustrated in a clinical trial in rheumatoid arthritis.Results: If the correlation between the outcome measured at baseline and at follow-up is rho, ANCOVA comparing groups of (1-rho(2))n subjects has the same power as t-test comparing groups of n subjects. When on the same data, ANCOVA is used instead of t-test, the precision of the treatment estimate is increased, and the length of the confidence interval is reduced by a factor root 1-rho(2).Conclusion: ANCOVA may considerably reduce the number of patients required for a trial. (C) 2007 Elsevier Inc. All rights reserved.