Analysing data from hormone-receptor assays.
Analysing data from hormone-receptor assays.
复制标题
分析激素受体测定的数据。
作者：
N. Cressie;D. Keightley
人类乳腺癌中激素受体的检测结果通常以Scatchard图的形式绘制。通常，通过最小二乘回归将直线拟合到数据点，从而计算出癌症中存在的结合位点的浓度。患者随后的激素治疗取决于这一值，因此有必要对其进行良好的估计。本研究探讨了三种表示数据的方法，即Scatchard图、倒数图和Woolf图，并研究了三种对每个图的数据点进行直线拟合的方法，即最小二乘回归、未加权稳健程序和加权稳健程序。当数据表现良好时，无论使用哪种回归技术，所有的曲线图都对结合位点的浓度给出了相似的答案。然而，当有多达三个离群点时，Scatchard曲线图，特别是使用最小二乘回归分析时，表现不佳。稳健回归分析在所有情节上都产生了更一致的结果；特别是迄今为止不受信任的倒数表在稳健回归下似乎表现良好，但其他证据表明情节不稳定。结论是，在这些实验中表示结合数据的最可靠的方式是通过伍尔夫图，并且随后的直线拟合通过未加权的稳健回归分析来使之具有抵抗力。
Assay results for hormone receptors in human breast cancer are generally plotted in the form of a Scatchard plot. Usually a line is fitted to the data points by least squares regression and thence the concentration of binding sites present in the cancer is calculated. The subsequent treatment of the patient with hormones depends on this value, so a good estimate of it is necessary. In this study, three methods of representing the data, namely the Scatchard plot, the reciprocal plot and the Woolf plot, were investigated, and three ways of fitting lines to the data points of each graph, namely least squares regression, an unweighted robust procedure and a weighted robust procedure, were examined. When the data were well-behaved all plots gave similar answers for the concentration of binding sites, irrespective of the regression technique used. However, when there were up to three outlying points, the Scatchard plot, particularly with least squares regression analysis, performed poorly. The robust regression analyses yielded more consistent results on all plots; in particular the hitherto mistrusted reciprocal scale seemed to perform well under robust regression, but other evidence indicated instability in the plot. It is concluded that the most reliable way of representing binding data in these experiments is by a Woolf plot, and that the subsequent line fitting is made resistant by an unweighted robust regression analysis.