Hsieh F Y, Lavori Philip W, Cohen Harvey J, Feussner John R
Department of Veterans Affairs, Palo Alto Health Care System (151-K), Palo Alto, CA 94304, USA.
Eval Health Prof. 2003 Sep;26(3):239-57. doi: 10.1177/0163278703255230.
For power and sample-size calculations, most practicing researchers rely on power and sample-size software programs to design their studies. There are many factors that affect the statistical power that, in many situations, go beyond the coverage of commercial software programs. Factors commonly known as design effects influence statistical power by inflating the variance of the test statistics. The authors quantify how these factors affect the variances so that researchers can adjust the statistical power or sample size accordingly. The authors review design effects for factorial design, crossover design, cluster randomization, unequal sample-size design, multiarm design, logistic regression, Cox regression, and the linear mixed model, as well as missing data in various designs. To design a study, researchers can apply these design effects, also known as variance inflation factors to adjust the power or sample size calculated from a two-group parallel design using standard formulas and software.
在进行功效和样本量计算时，大多数执业研究人员依靠功效和样本量软件程序来设计他们的研究。有许多因素会影响统计功效，在许多情况下，这些因素超出了商业软件程序的涵盖范围。通常被称为设计效应的因素会通过夸大检验统计量的方差来影响统计功效。作者对这些因素如何影响方差进行了量化，以便研究人员能够相应地调整统计功效或样本量。作者回顾了析因设计、交叉设计、整群随机化、不等样本量设计、多臂设计、逻辑回归、Cox回归和线性混合模型的设计效应，以及各种设计中的缺失数据。为了设计一项研究，研究人员可以应用这些设计效应，也称为方差膨胀因子，来调整使用标准公式和软件从两组平行设计计算出的功效或样本量。
