Assessment of skewed exposure in case-control studies with pooling.
Assessment of skewed exposure in case-control studies with pooling.
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DOI：
10.1002/sim.5351
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发表时间：
2012-09-28
影响因子：
2
通讯作者：
Lyles, Robert H.
中科院分区：
文献类型：
--
作者：
Whitcomb, Brian W.;Perkins, Neil J.;Zhang, Zhiwei;Ye, Aijun;Lyles, Robert H.
已提出将来自多个参与者的联合收割机样品组合用于实验室测定的基于合并的策略用于生物标志物的流行病学调查，以解决包括成本、效率、检测以及何时可获得最小样品体积的问题。先前已经描述了标准逻辑回归模型的修改，以允许使用合并数据;然而，该模型对暴露分布和风险的对数线性进行了假设（即，常比值比），在实践中可能会被违反。我们的动机是一个嵌套的病例对照研究流产和炎症因素与高度偏态分布，以开发一个更灵活的模型，分析汇总数据。使用伽玛分布的特性和暴露条件下的二进制结果和暴露条件下的结果的模型之间的关系，我们使用修改后的逻辑回归，以适应非线性由于不等的形状参数在伽玛分布的情况下，控制暴露。使用模拟，我们比较我们的方法与现有的方法逻辑回归的汇总数据考虑：1。恒定和剂量依赖性效应; 2.伽马和对数正态分布暴露; 3.效果大小; 4。合并生物标本的比例。我们表明，我们的方法允许估计的比值比，随着暴露水平的变化，但有最小的损失效率相比，现有的方法时，暴露的影响是剂量不变的。我们的模型在偏倚和效率方面与最大似然估计方法相似，并且当效应在暴露期间可能不恒定时，提供了一种易于实施的方法来估计合并的生物标志物数据。
Pooling based strategies that combine samples from multiple participants for laboratory assays have been proposed for epidemiologic investigations of biomarkers to address issues including cost, efficiency, detection, and when minimal sample volume is available. A modification of the standard logistic regression model has been previously described to allow use with pooled data; however, this model makes assumptions regarding exposure distribution and logit-linearity of risk (i.e., constant odds ratio) that can be violated in practice. We were motivated by a nested case-control study of miscarriage and inflammatory factors with highly skewed distributions to develop a more flexible model for analysis of pooled data. Using characteristics of the gamma distribution and the relation between models of binary outcome conditional on exposure and of exposure conditional on outcome, we use a modified logistic regression to accommodate non-linearity due to unequal shape parameters in gamma distributed exposure for cases and controls. Using simulations, we compare our approach with existing methods for logistic regression for pooled data considering: 1. Constant and dose-dependent effects; 2. Gamma and log-normal distributed exposure; 3. Effect size, and; 4. The proportions of biospecimens pooled. We show that our approach allows estimation of odds ratios that vary with exposure level, yet has minimal loss of efficiency compared to existing approaches when exposure effects are dose-invariant. Our model performed similarly to a maximum likelihood estimation approach in terms of bias and efficiency, and provides an easily implemented approach for estimation with pooled biomarker data when effects may not be constant across exposure.
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