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年龄依赖性生化指标:一种计算参考区间的方法。

Age-dependent biochemical quantities: an approach for calculating reference intervals.

作者信息

Bjerner J

机构信息

Central Laboratory, Norwegian Radium Hospital, Montebello, Oslo, Norway.

出版信息

Scand J Clin Lab Invest. 2007;67(7):707-22. doi: 10.1080/00365510701342070.

Abstract

OBJECTIVE

A parametric method is often preferred when calculating reference intervals for biochemical quantities, as non-parametric methods are less efficient and require more observations/study subjects. Parametric methods are complicated, however, because of three commonly encountered features. First, biochemical quantities seldom display a Gaussian distribution, and there must either be a transformation procedure to obtain such a distribution or a more complex distribution has to be used. Second, biochemical quantities are often dependent on a continuous covariate, exemplified by rising serum concentrations of MUC1 (episialin, CA15.3) with increasing age. Third, outliers often exert substantial influence on parametric estimations and therefore need to be excluded before calculations are made.

MATERIAL AND METHODS

The International Federation of Clinical Chemistry (IFCC) currently recommends that confidence intervals be calculated for the reference centiles obtained. However, common statistical packages allowing for the adjustment of a continuous covariate do not make this calculation.

RESULTS

In the method described in the current study, Tukey's fence is used to eliminate outliers and two-stage transformations (modulus-exponential-normal) in order to render Gaussian distributions. Fractional polynomials are employed to model functions for mean and standard deviations dependent on a covariate, and the model is selected by maximum likelihood. Confidence intervals are calculated for the fitted centiles by combining parameter estimation and sampling uncertainties. Finally, the elimination of outliers was made dependent on covariates by reiteration.

CONCLUSIONS

Though a good knowledge of statistical theory is needed when performing the analysis, the current method is rewarding because the results are of practical use in patient care.

摘要

目的

在计算生化指标的参考区间时,参数法通常更受青睐,因为非参数法效率较低且需要更多的观察对象/研究受试者。然而,参数法很复杂,原因有三个常见特征。首先,生化指标很少呈现高斯分布,因此必须有一个转换程序来获得这种分布,或者必须使用更复杂的分布。其次,生化指标通常依赖于一个连续协变量,例如随着年龄增长,血清MUC1(上皮涎蛋白,CA15.3)浓度升高。第三,异常值通常会对参数估计产生重大影响,因此在进行计算之前需要将其排除。

材料与方法

国际临床化学联合会(IFCC)目前建议对获得的参考百分位数计算置信区间。然而,常见的允许调整连续协变量的统计软件包并未进行此计算。

结果

在本研究中描述的方法中,使用Tukey界限来消除异常值,并采用两阶段转换(模-指数-正态)以呈现高斯分布。采用分数多项式对依赖于协变量的均值和标准差函数进行建模,并通过最大似然法选择模型。通过结合参数估计和抽样不确定性,为拟合的百分位数计算置信区间。最后,通过迭代使异常值的消除依赖于协变量。

结论

虽然进行分析时需要对统计理论有很好的了解,但当前方法是值得的,因为结果在患者护理中具有实际用途。

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