Estimation of the disease-specific diagnostic marker distribution under verification bias.

Estimation of the disease-specific diagnostic marker distribution under verification bias.
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在验证偏置下的疾病特异性诊断标记分布的估计。

DOI:
10.1016/j.csda.2008.06.021
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发表时间:
2009-01-15
影响因子:
1.8
通讯作者:
Rotnitzky A
Rotnitzky A
中科院分区:
数学3区
文献类型:
--
作者:
Page JH;Rotnitzky A

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在给定协变量和疾病状态的情况下,我们考虑对诊断标记物的条件分布的参数模型进行索引的参数估计。这种模型对于评估标记物是否以及在多大程度上准确检测或丢弃疾病的能力取决于患者的特征是有用的。一个经常使模型参数估计复杂化的问题是,估计必须从观测研究中进行。通常,在这样的研究中,并不是所有的患者都接受了疾病的黄金标准评估。此外,患者是否接受验证的决定不受研究设计的控制。在这种情况下,基于观察到的疾病状态的受试者的最大似然估计器通常是有偏差的。在这篇文章中,我们提出了模型参数的估计器,这些参数可以根据测量的患者特征进行选择和验证,并另外根据假设的残差关联度进行调整。这种估计器可用作残差关联度的敏感性分析的一部分。我们描述了一种双重稳健估计器,如果用于选择验证的概率的模型或用于被验证的受试者中的疾病概率的模型(但不一定两者都是)是正确的,则具有一致的吸引人的特征。
We consider the estimation of the parameters indexing a parametric model for the conditional distribution of a diagnostic marker given covariates and disease status. Such models are useful for the evaluation of whether and to what extent a marker’s ability to accurately detect or discard disease depends on patient characteristics. A frequent problem that complicates the estimation of the model parameters is that estimation must be conducted from observational studies. Often, in such studies not all patients undergo the gold standard assessment of disease. Furthermore, the decision as to whether a patient undergoes verification is not controlled by study design. In such scenarios, maximum likelihood estimators based on subjects with observed disease status are generally biased. In this paper, we propose estimators for the model parameters that adjust for selection to verification that may depend on measured patient characteristics and additonally adjust for an assumed degree of residual association. Such estimators may be used as part of a sensitivity analysis for plausible degrees of residual association. We describe a doubly robust estimator that has the attractive feature of being consistent if either a model for the probability of selection to verification or a model for the probability of disease among the verified subjects (but not necessarily both) is correct.
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发表时间: 1998-01-01
影响因子: 0.8
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发表时间: 2003-03-01
期刊: BIOMETRICS
影响因子: 1.9
作者:
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通讯作者: Barnhart, HX