Zhou X H
Department of Medicine, Indiana University School of Medicine, Indianapolis.
Stat Med. 1994 Sep 15;13(17):1737-45. doi: 10.1002/sim.4780131705.
The pairing of sensitivity and specificity expresses the efficacy of a test, and positive and negative predictive values measure the accuracy of a diagnostic test when applied to a particular patient. To calculate these measures, one has to know the true disease status of each patient. In practice, however, some patients may not be selected for verification of disease status. It has been shown that the estimated sensitivity and specificity may be biased if one includes in the study sample only the patients with verified disease statuses. This paper concerns the properties of the estimators of positive and negative predictive values using only patients with verified disease statuses. First, I show that these estimators are unbiased and provide consistent estimators for the variances of these estimators under the assumption that the probability of selecting a patient for a disease verification procedure does not depend directly on the true disease status of the patient. Then, I use the ML method to study the sensitivity of the naive estimators to the departure from the conditional independence assumption.
灵敏度和特异度的配对体现了一项检测的效能，而阳性预测值和阴性预测值则衡量了诊断检测应用于特定患者时的准确性。为计算这些指标，必须知晓每位患者的真实疾病状态。然而在实际中，部分患者可能未被选来核实疾病状态。研究表明，如果仅将已核实疾病状态的患者纳入研究样本，那么估计的灵敏度和特异度可能会有偏差。本文探讨仅使用已核实疾病状态的患者时阳性预测值和阴性预测值估计量的性质。首先，我证明这些估计量是无偏的，并在选择患者进行疾病核实程序的概率不直接取决于患者真实疾病状态这一假设下，为这些估计量的方差提供一致估计量。然后，我使用极大似然法研究朴素估计量对偏离条件独立性假设的敏感性。
