A semiparametric mixture approach to case-control studies with errors in covariables
A semiparametric mixture approach to case-control studies with errors in covariables
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DOI：
10.2307/2291667
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发表时间：
1996-06-01
影响因子：
3.7
通讯作者：
Lindsay, BG
中科院分区：
文献类型：
--
作者：
Roeder, K;Carroll, RJ;Lindsay, BG
设计了在病例对照研究中估计具有依赖于协变量X的二分反应D的前瞻性logistic模型的参数的方法。对于一部分样本，金标准X和替代协变量W都可用;然而，对于大部分数据，只有替代协变量W可用。通过使用混合模型，可以为两种类型的数据适当地建模真实协变量和响应之间的关系。可能性取决于X的边缘分布和测量误差密度（W\X，D）。后者是基于验证样本的参数化建模。真实协变量的边缘分布使用非参数混合分布建模。通过这种方式，我们可以提高效率，减少参数估计的偏差，结果也适用于没有验证数据时，提供的误差分布是已知的或从独立的数据源估计。许多结果也适用于更容易的前瞻性抽样。
Methods are devised for estimating the parameters of a prospective logistic model in a case-control study with dichotomous response D that depends on a covariate X. For a portion of the sample, both the gold standard X and a surrogate covariate W are available; however, for the greater portion of the data, only the surrogate covariable W is available. By using a mixture model, the relationship between the true covariable and the response can be modeled appropriately for both types of data. The likelihood depends on the marginal distribution of X and the measurement error density (W\X,D). The latter is modeled parametrically based on the validation sample. The marginal distribution of the true covariable is modeled using a nonparametric mixture distribution. In this way we can improve the efficiency and reduce the bias of the parameter estimates, The results also apply when there is no validation data provided the error distribution is known or estimated from an independent data source. Many of the results also apply to the easier case of prospective sampling.