Power and sample size calculations for generalized regression models with covariate measurement error
Power and sample size calculations for generalized regression models with covariate measurement error
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DOI：
10.1002/sim.1388
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发表时间：
2003-04-15
影响因子：
2
通讯作者：
Karagas, M
中科院分区：
文献类型：
--
作者：
Tosteson, TD;Buzas, JS;Karagas, M
协变量测量误差通常是用于回归建模的科学数据的一个特征。此类错误的后果包括失去与真实协变量相对应的回归参数的显着性检验的功效。忽略协变量测量误差的功效和样本量计算往往会高估功效并低估实现所需功效所需的实际样本量。在本文中，我们使用基于拟似然方法的广义分数测试推导了广义线性模型的一种新颖的测量误差校正幂函数。我们的幂函数非常灵活，因为它适用于具有离散或连续标量协变量（曝光）的设计，可以在有误差或无误差的情况下进行测量，允许额外的混杂变量，并适用于广泛的广义回归和测量误差模型。描述了一个程序，该程序为连续暴露提供样本量或功效，并具有正态测量误差模型和逻辑回归中的单个正态混杂变量。我们通过模拟和数值研究展示了功率计算的改进特性。一个正在进行的癌症与砷暴露研究的例子，通过脚趾甲浓度和自来水样本进行测量。版权所有 (C) 2003 John Wiley Sons, Ltd.
Covariate measurement error is often a feature of scientific data used for regression modelling. The consequences of such errors include a loss of power of tests of significance for the regression parameters corresponding to the true covariates. Power and sample size calculations that ignore covariate measurement error tend to overestimate power and underestimate the actual sample size required to achieve a desired power. in this paper we derive a novel measurement error corrected power function for generalized linear models using a generalized score test based on quasi-likelihood methods. Our power function is flexible in that it is adaptable to designs with a discrete or continuous scalar covariate (exposure) that can be measured with or without error, allows for additional confounding variables and applies to a broad class of generalized regression and measurement error models. A program is described that provides sample size or power for a continuous exposure with a normal measurement error model and a single normal confounder variable in logistic regression. We demonstrate the improved properties of our power calculations with simulations and numerical studies. An example is given from an ongoing study of cancer and exposure to arsenic as measured by toenail concentrations and tap water samples. Copyright (C) 2003 John Wiley Sons, Ltd.