Stöckl D, Dewitte K, Thienpont L M
Laboratorium voor Analytische Chemie, Faculteit der Farmaceutische Wetenschappen, Universiteit Gent, Belgium.
Clin Chem. 1998 Nov;44(11):2340-6.
We compared the application of ordinary linear regression, Deming regression, standardized principal component analysis, and Passing-Bablok regression to real-life method comparison studies to investigate whether the statistical model of regression or the analytical input data have more influence on the validity of the regression estimates. We took measurements of serum potassium as an example for comparisons that cover a narrow data range and measurements of serum estradiol-17beta as an example for comparisons that cover a wide data range. We demonstrate that, in practice, it is not the statistical model but the quality of the analytical input data that is crucial for interpretation of method comparison studies. We show the usefulness of ordinary linear regression, in particular, because it gives a better estimate of the standard deviation of the residuals than the other procedures. The latter is important for distinguishing whether the observed spread across the regression line is caused by the analytical imprecision alone or whether sample-related effects also contribute. We further demonstrate the usefulness of linear correlation analysis as a first screening test for the validity of linear regression data. When ordinary linear regression (in combination with correlation analysis) gives poor estimates, we recommend investigating the analytical reason for the poor performance instead of assuming that other linear regression procedures add substantial value to the interpretation of the study. This investigation should address whether (a) the x and y data are linearly related; (b) the total analytical imprecision (s(a,tot)) is responsible for the poor correlation; (c) sample-related effects are present (standard deviation of the residuals >> s(a,tot)); (d) the samples are adequately distributed over the investigated range; and (e) the number of samples used for the comparison is adequate.
我们将普通线性回归、Deming回归、标准化主成分分析和Passing-Bablok回归应用于实际的方法比较研究，以调查回归统计模型或分析输入数据对回归估计有效性的影响更大。我们以血清钾测量为例进行窄数据范围的比较，以血清雌二醇-17β测量为例进行宽数据范围的比较。我们证明，在实际中，对于方法比较研究的解释而言，关键的不是统计模型，而是分析输入数据的质量。我们特别展示了普通线性回归的有用性，因为它比其他方法能更好地估计残差的标准差。后者对于区分观察到的回归线离散是仅由分析不精密度引起还是样本相关效应也有贡献很重要。我们进一步证明了线性相关分析作为线性回归数据有效性的初步筛选测试的有用性。当普通线性回归（结合相关分析）给出较差估计时，我们建议调查性能不佳的分析原因，而不是假设其他线性回归程序能为研究解释增加很大价值。该调查应关注：(a)x和y数据是否线性相关；(b)总分析不精密度(s(a,tot))是否导致相关性差；(c)是否存在样本相关效应（残差标准差>>s(a,tot)）；(d)样本在研究范围内分布是否充分；以及(e)用于比较的样本数量是否足够。
