Power and Sample Size Methods for Mammography Trials

由于比较数字和模拟乳房X光检查的试验依赖于复杂的设计，耗资巨大，而且涉及许多女性的健康，因此准确的功率和样本量计算至关重要。在这些试验中，放射科医生使用不同类型的数字机器和不同形式的图像处理来阅读许多胶片。检测准确率的衡量标准是命中率和误警率。每个医生都会生成几个准确度分数，每个分数对应一种情况。这些指标是相互关联的。如果使用模拟机器的精度衡量标准来预测数字机器的精度，则会增加另一层复杂性。这个实验设计导致分析一个通用的线性多变量模型，用于重复测量，包括固定和随机预测，以及每个单元中的完整模型。对于本设计，提出了几种功率计算方法。可以为一组特定的预测器值计算条件幂。无条件功率是有条件功率相对于随机预测器的所有可能的随机实现的期望值。分位数功率是具有非中心性的条件功率，该非中心性对应于以指定概率发生的随机预测器的实现。理论推导将导致易于使用的软件和描述性的例子。这些将为放射科医生提供快速计算的工具，以及确定样本大小的交互式、图形化方法。

Because trials to compare digital and analog mammography rely on complex designs, cost large amounts of money and involve the health of many women, accurate power and sample size calculations are essential. In these trials, radiologists read many films using different sorts of digital machines, and with varying forms of image processes. Measures of the accuracy of detection are the hit rate, and the false alarm rate. Each doctor generates several accuracy scores, one for each condition. These measures are correlated. If one uses an accuracy measure for analog machines as a predictor of accuracy on digital machines, another layer of complexity is added. This experimental design leads to analyzing a General Linear Multivariate Model for repeated measures, both fixed and random predictors, and a full model in every cell. For this design, several sorts of power calculations are proposed. The conditional power can be calculated for a specific set of predictor values. Unconditional power is the expected value of conditional power over all possible stochastic realizations of the random predictors. Quantile power is the conditional power with the noncentrality that corresponds to a realization of the random predictors that occurs with specified probability. The theoretical derivations will lead to easily used software and descriptive examples. These will give radiologists tools for rapid calculation and an interactive, graphical approach to sample size determination.