Borm George F, Fransen Jaap, Lemmens Wim A J G
Department of Epidemiology and Biostatistics, Radboud University Nijmegen Medical Centre, Geert Grooteplein 21, PO Box 9101, NL-6500 HB Nijmegen, The Netherlands.
J Clin Epidemiol. 2007 Dec;60(12):1234-8. doi: 10.1016/j.jclinepi.2007.02.006. Epub 2007 Jun 6.
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.
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.
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 1-rho(2).
ANCOVA may considerably reduce the number of patients required for a trial.
比较两种治疗方法对连续结果影响的随机临床试验可采用协方差分析（ANCOVA）或t检验方法进行分析。我们提出了一种在使用ANCOVA时计算样本量的方法。
我们推导了一个近似样本量公式。通过模拟来验证该公式的准确性，并改进小样本试验的近似值。在类风湿性关节炎的一项临床试验中展示了样本量的计算。
如果基线测量结果与随访测量结果之间的相关性为rho，比较(1 - rho(2))n名受试者组的ANCOVA与比较n名受试者组的t检验具有相同的检验效能。在相同数据上，使用ANCOVA而非t检验时，治疗估计的精度会提高，置信区间的长度会缩短1 - rho(2)倍。
ANCOVA可能会显著减少试验所需的患者数量。
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