A conditional likelihood approach for regression analysis using biomarkers measured with batch-specific error.
A conditional likelihood approach for regression analysis using biomarkers measured with batch-specific error.
复制标题
使用通过批次特定误差测量的生物标志物进行回归分析的条件似然方法。
DOI：
10.1002/sim.5473
复制
发表时间：
2012
影响因子：
2
通讯作者：
Long,Qi
中科院分区：
文献类型：
--
作者：
Wang,Ming;Flanders,WDana;Bostick,RoberdM;Long,Qi
测量误差在流行病学和生物医学研究中很常见。当分批或成组测量生物标志物时，测量误差在每个批次或组内可能相关。在回归分析中，大多数现有方法不适用于预测变量中存在批次特定测量误差的情况。我们提出了一种鲁棒的条件似然方法来解释预测变量中的批次特定误差，当批次效应是加性的并且是主要的误差来源时，该方法不需要对测量误差的分布进行假设。虽然批次作为分类协变量的回归模型产生与线性回归的条件似然方法相同的参数估计值，但这一结果并不适用于所有广义线性模型，特别是logistic回归。我们的模拟研究表明，条件似然方法实现了更好的有限样本性能比回归校准方法或天真的方法，而无需调整测量误差。在逻辑回归的情况下，我们提出的方法也优于以批次作为分类协变量的回归方法。此外，我们还研究了一种结合条件似然法和回归校准法的“混合”方法，该方法在模拟中显示出在存在批次特定和测量特定误差的情况下具有良好的性能。我们通过使用结直肠腺瘤研究的数据来说明我们的方法。版权所有© 2012约翰威利父子有限公司.
Measurement error is common in epidemiological and biomedical studies. When biomarkers are measured in batches or groups, measurement error is potentially correlated within each batch or group. In regression analysis, most existing methods are not applicable in the presence of batch‐specific measurement error in predictors. We propose a robust conditional likelihood approach to account for batch‐specific error in predictors when batch effect is additive and the predominant source of error, which requires no assumptions on the distribution of measurement error. Although a regression model with batch as a categorical covariable yields the same parameter estimates as the proposed conditional likelihood approach for linear regression, this result does not hold in general for all generalized linear models, in particular, logistic regression. Our simulation studies show that the conditional likelihood approach achieves better finite sample performance than the regression calibration approach or a naive approach without adjustment for measurement error. In the case of logistic regression, our proposed approach is shown to also outperform the regression approach with batch as a categorical covariate. In addition, we also examine a ‘hybrid’ approach combining the conditional likelihood method and the regression calibration method, which is shown in simulations to achieve good performance in the presence of both batch‐specific and measurement‐specific errors. We illustrate our method by using data from a colorectal adenoma study. Copyright © 2012 John Wiley & Sons, Ltd.