Mixed-effects Gaussian process functional regression models with application to dose-response curve prediction
Mixed-effects Gaussian process functional regression models with application to dose-response curve prediction
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DOI：
10.1002/sim.4502
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发表时间：
2012-11-20
影响因子：
2
通讯作者：
West, R. M.
中科院分区：
文献类型：
--
作者：
Shi, J. Q.;Wang, B.;West, R. M.
将参数混合效应模型与非参数高斯过程回归模型相结合，提出了一种新的半参数函数回归分析模型，即混合效应高斯过程函数回归模型.参数分量可以提供响应和协变量之间的解释性信息，而非参数分量可以添加非线性。我们可以同时对均值和协方差结构进行建模，将从其他受试者那里借来的信息与从每个个体受试者那里收集到的信息结合起来。我们将该模型应用于描述受试者对不同剂量药物或药剂的反应变化的剂量反应曲线，并在许多领域具有广泛的应用。我们说明了肾性贫血的管理方法。当随着时间的推移，该机制包括来自受试者/患者的更多信息时，个体剂量反应曲线得到改善，从而实现患者特异性治疗方案。版权所有（c）2012约翰威利父子有限公司
We propose a new semiparametric model for functional regression analysis, combining a parametric mixed-effects model with a nonparametric Gaussian process regression model, namely a mixed-effects Gaussian process functional regression model. The parametric component can provide explanatory information between the response and the covariates, whereas the nonparametric component can add nonlinearity. We can model the mean and covariance structures simultaneously, combining the information borrowed from other subjects with the information collected from each individual subject. We apply the model to doseresponse curves that describe changes in the responses of subjects for differing levels of the dose of a drug or agent and have a wide application in many areas. We illustrate the method for the management of renal anaemia. An individual doseresponse curve is improved when more information is included by this mechanism from the subject/patient over time, enabling a patient-specific treatment regime. Copyright (c) 2012 John Wiley & Sons, Ltd.