Dunson David B, Watson M, Taylor Jack A
Biostatistics Branch, MD A3-03, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709, USA.
Biometrics. 2003 Jun;59(2):296-304. doi: 10.1111/1541-0420.00036.
Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions about residual densities by relying on an approximation based on quantiles. To accommodate within-subject dependency, the quantile response categories of the surrogate outcomes are related to underlying normal variables, which depend on a latent normal response. This underlying Gaussian covariance structure simplifies interpretation and model fitting, without restricting the marginal densities of the surrogate outcomes. A Markov chain Monte Carlo algorithm is proposed for posterior computation, and the methods are applied to single-cell electrophoresis (comet assay) data from a genetic toxicology study.
通常，感兴趣的反应无法直接测量，因此有必要依赖多个替代指标，在给定潜在反应和观察到的协变量的情况下，可以假定这些替代指标是条件独立的。潜在反应模型通常假定残差密度服从高斯分布。本文提出了一种贝叶斯中位数回归建模方法，该方法通过基于分位数的近似来避免对残差密度的参数假设。为了适应受试者内部的依赖性，替代结果的分位数反应类别与潜在的正态变量相关，这些变量依赖于潜在的正态反应。这种潜在的高斯协方差结构简化了解释和模型拟合，同时不限制替代结果的边际密度。提出了一种马尔可夫链蒙特卡罗算法用于后验计算，并将这些方法应用于遗传毒理学研究中的单细胞电泳（彗星试验）数据。
