Devine Owen
Eval Health Prof. 2003 Sep;26(3):315-39. doi: 10.1177/0163278703255232.
The author presents two examples illustrating the bias in sample-size estimates that can result from ignoring measurement error among study variables. The first example examines the impact of ignoring misclassification of the study's outcome variable on the accuracy of sample-size estimates. In addition, the author outlines a simple yet effective means of adjusting sample-size estimates to account for outcome misclassification. In the second example, the author illustrates the potential for severe underestimation of required sample size in studies using linear regression to evaluate associations between the outcome of interest and an independent variable subject to classical measurement error. The author concludes with a discussion of pertinent literature that might be helpful to study planners interested in adjusting sample-size estimates to account for measurement errors in both outcome and predictor variables.
作者给出了两个例子，说明在样本量估计中因忽略研究变量间的测量误差而可能产生的偏差。第一个例子考察了忽略研究结果变量的错误分类对样本量估计准确性的影响。此外，作者概述了一种简单而有效的方法来调整样本量估计，以考虑结果的错误分类。在第二个例子中，作者说明了在使用线性回归评估感兴趣的结果与存在经典测量误差的自变量之间的关联时，所需样本量可能被严重低估的情况。作者最后讨论了相关文献，这可能有助于有兴趣调整样本量估计以考虑结果变量和预测变量测量误差的研究规划者。
