A weighted generalized score statistic for comparison of predictive values of diagnostic tests.
A weighted generalized score statistic for comparison of predictive values of diagnostic tests.
复制标题
加权广义分数统计量，用于比较诊断测试的预测值。
DOI：
10.1002/sim.5587
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发表时间：
2013-03-15
影响因子：
2
通讯作者：
Kosinski, Andrzej S.
中科院分区：
文献类型：
--
作者：
Kosinski, Andrzej S.
关键词：
阳性和阴性预测值是衡量医学诊断试验性能的重要指标。我们考虑在所有患者接受两次诊断测试的配对设计中，两个阳性或两个阴性预测值的检测相等。现有的预测值检验相等性的统计检验要么是基于多项分布的Wald检验，要么是广义估计方程（GEE）框架下的经验Wald和广义分数检验。正如在文献中提出的，这些检验统计量有相当复杂的公式，没有清晰的直观洞察力。我们提出了数学上等价但代数上简单直观的它们的重新表述。从我们提出的一个新的重新表述中可以清楚地看到，在独立样本情况下，广义分数统计量并不总是减少到常用的分数统计量。为了缓解这一问题，我们引入了加权广义分数（WGS）检验统计量，该统计量将经验协方差矩阵与新提出的权重相结合。该统计量计算简单，在独立样本情况下总是简化为分数统计量，并且模拟表明，它比其他统计量更好地保留了I型误差。因此，我们认为提出的WGS统计量是检验两个预测值相等性和相应样本量计算的首选统计量。新的瓦尔德统计量公式可以方便地计算预测值差异的置信区间。所介绍的概念有可能导致在一般的GEE设置加权广义分数测试统计的发展。
Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations which are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we present, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic which incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, it always reduces to the score statistic in the independent samples situation, and it preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the weighted generalized score test statistic in a general GEE setting.
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