Tu X M, Kowalski J, Crits-Christoph P, Gallop R
Department of Biostatistics and Computational Biology, University of Rochester, NY 14642, USA.
Stat Med. 2006 Aug 15;25(15):2587-606. doi: 10.1002/sim.2273.
Power analysis constitutes an important component of modern clinical trials and research studies. Although a variety of methods and software packages are available, almost all of them are focused on regression models, with little attention paid to correlation analysis. However, the latter is arguably a simpler and more appropriate approach for modelling concurrent events, especially in psychosocial research. In this paper, we discuss power and sample size estimation for correlation analysis arising from clustered study designs. Our approach is based on the asymptotic distribution of correlated Pearson-type estimates. Although this asymptotic distribution is easy to use in data analysis, the presence of a large number of parameters creates a major problem for power analysis due to the lack of real data to estimate them. By introducing a surrogacy-type assumption, we show that all nuisance parameters can be eliminated, making it possible to perform power analysis based only on the parameters of interest. Simulation results suggest that power and sample size estimates obtained under the proposed approach are robust to this assumption.
功效分析是现代临床试验和研究的重要组成部分。尽管有多种方法和软件包可供使用，但几乎所有这些方法和软件包都侧重于回归模型，而对相关分析关注甚少。然而，对于同时发生事件的建模，相关分析可以说是一种更简单、更合适的方法，尤其是在心理社会研究中。在本文中，我们讨论了聚类研究设计中相关分析的功效和样本量估计。我们的方法基于相关Pearson型估计的渐近分布。虽然这种渐近分布在数据分析中易于使用，但由于缺乏估计大量参数的实际数据，大量参数的存在给功效分析带来了一个主要问题。通过引入替代型假设，我们表明所有干扰参数都可以消除，从而仅基于感兴趣的参数进行功效分析成为可能。模拟结果表明，在所提出的方法下获得的功效和样本量估计对该假设具有鲁棒性。
