Department of Epidemiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
Stat Med. 2012 Sep 10;31(20):2169-78. doi: 10.1002/sim.5352. Epub 2012 Apr 11.
For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An analysis of covariance (ANCOVA) using both the baseline and follow-up score of the outcome will then have more power. We calculate the efficiency of an ANCOVA analysis using the baseline scores compared with an analysis on follow-up scores only. The sample size for such an ANCOVA analysis is a factor r2 smaller, where r is the correlation of the cluster means between baseline and follow-up. This correlation can be expressed in clinically interpretable parameters: the correlation between baseline and follow-up of subjects (subject autocorrelation) and that of clusters (cluster autocorrelation). Because of this, subject matter knowledge can be used to provide (range of) plausible values for these correlations, when estimates from previous studies are lacking. Depending on how large the subject and cluster autocorrelations are, analysis of covariance can substantially reduce the number of clusters needed.
对于连续结局的整群随机试验，样本量通常按照分析治疗期末（随访评分）结局的方法进行计算。然而，通常可以获得结局的基线测量值或能够获得该值。此时采用基线和随访评分的协方差分析（ANCOVA）将具有更大的效能。我们通过比较仅分析随访评分的方法，计算使用基线评分的 ANCOVA 分析的效率。对于这种 ANCOVA 分析，样本量要小 r2 倍，其中 r 是基线和随访时群组均值间的相关性。该相关性可以用临床可解释的参数表示：受试者间（受试者自相关）和群组间（群组自相关）的基线和随访的相关性。因此，当缺乏来自先前研究的估计值时，可以使用主题知识来提供这些相关性的（范围）合理值。根据受试者和群组自相关的大小，协方差分析可以大大减少所需的群组数量。
