Approximate and Pseudo-Likelihood Analysis for Logistic Regression Using External Validation Data to Model Log Exposure.
Approximate and Pseudo-Likelihood Analysis for Logistic Regression Using External Validation Data to Model Log Exposure.
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DOI：
10.1007/s13253-012-0115-9
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发表时间：
2013-03-01
影响因子：
1.4
通讯作者：
Kupper, Lawrence L.
中科院分区：
文献类型：
--
作者：
Lyles, Robert H.;Kupper, Lawrence L.
环境流行病学研究的一个共同目标是进行逻辑回归建模，将暴露的连续测量与二元疾病状态相关联，并对协变量进行调整。一个常见的并发症是，暴露可能只能通过一系列假设与之相关的受试者特定变量来间接测量。受一项调查肺功能与金属工作液暴露之间关系的具体研究的启发，我们重点关注乘数-对数正态结构测量误差场景以及在外部验证数据可用时解决该问题的方法。从概念上讲，我们强调的情况下，真正的未转换的曝光是感兴趣的建模疾病状态，但测量误差是加性的对数尺度，因此乘法的原始规模。在方法上，我们赞成伪似然（PL）的方法，表现出更少的计算问题比直接完全最大似然（ML），但保持一致性的假设模型，而不需要小的曝光效应和/或小的测量误差假设。这些假设是计算方便的替代方法所需要的，如回归校准（RC）和基于概率单位近似的ML。我们总结了模拟显示相当大的潜力，在后两种方法的偏见，同时支持在各种情况下使用PL。我们还提供了获得调整后的标准误伴随RC和PL估计的可访问的策略。
A common goal in environmental epidemiologic studies is to undertake logistic regression modeling to associate a continuous measure of exposure with binary disease status, adjusting for covariates. A frequent complication is that exposure may only be measurable indirectly, through a collection of subject-specific variables assumed associated with it. Motivated by a specific study to investigate the association between lung function and exposure to metal working fluids, we focus on a multiplicative-lognormal structural measurement error scenario and approaches to address it when external validation data are available. Conceptually, we emphasize the case in which true untransformed exposure is of interest in modeling disease status, but measurement error is additive on the log scale and thus multiplicative on the raw scale. Methodologically, we favor a pseudo-likelihood (PL) approach that exhibits fewer computational problems than direct full maximum likelihood (ML) yet maintains consistency under the assumed models without necessitating small exposure effects and/or small measurement error assumptions. Such assumptions are required by computationally convenient alternative methods like regression calibration (RC) and ML based on probit approximations. We summarize simulations demonstrating considerable potential for bias in the latter two approaches, while supporting the use of PL across a variety of scenarios. We also provide accessible strategies for obtaining adjusted standard errors to accompany RC and PL estimates.
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