基准剂量估计的非参数贝叶斯方法研究及应用

发布时间：2018-04-29 01:25

本文选题：基准剂量 + BMDS软件　；参考：《山西医科大学》2016年硕士论文

【摘要】：目的:本文将介绍两种估计基准剂量的非参数贝叶斯方法:基于加权过程的非参数贝叶斯方法和基于随机过程的非参数贝叶斯方法,将这两种方法相互比较后再与参数模型作比较,研究其在不同剂量反应数据情形下的表现。方法:分别介绍常用的参数模型、基于加权过程的非参数贝叶斯方法和基于随机过程的非参数贝叶斯方法的基本原理。通过不同的参数设置构建8种情形的剂量反应数据,采用R软件生成模拟数据并用介绍的两种非参数方法做模拟分析,以BMD估计值与真实值的差距以及BMDL的覆盖率为评价指标,比较两种方法的表现。从文献中选取9组癌症数据,分别用两种非参数方法和BMDS软件中的9种参数模型进行估计,比较各方法估计结果的差异。结果:两种非参数方法NPB1和NPB2的估计结果都与真实BMD值较为接近。当多阶段模型参数设置为MS(0,1,1,3),剂量组数为6,剂量间距为对数间距时,NPB1方法的BMDL覆盖率明显低于正常水平,BMD估计值有较大偏倚,这种较大的偏倚可能是由相对高的后验宽度参数值带来的过度拟合而造成;两种方法的RMSE值均较小;NPB2方法的BMDL覆盖率更接近真实水平,保守的来讲,NPB2方法较NPB1方法更为可取。实例中与不同的参数模型结果相比可知,两种非参数方法得到的BMD估计值都落入或非常接近由参数模型得到的BMD估计值的范围;而在可信区间方面,非参数方法估计的BMDLs值大多比参数模型算得的BMDLs值要小。结论:两种非参数贝叶斯方法在基准剂量估计过程中都可提供合理的拟合值,尤其是在传统的参数模型无法提供合理的拟合值的情况下,也可很好的应用;而NPB2方法在估计结果和软件运算速度方面均略优于NPB1。非参数方法在模型拟合过程中很灵活,应用范围广泛,在今后基准剂量估计研究中,是一种非常有用的方法。
[Abstract]:Objective: this paper will introduce two kinds of nonparametric Bayesian methods for estimating baseline dose: nonparametric Bayesian method based on weighted process and nonparametric Bayesian method based on stochastic process. The two methods were compared with each other and then compared with the parametric model to study their performance under different dose-response data. Methods: the basic principles of parametric model, nonparametric Bayesian method based on weighted process and nonparametric Bayesian method based on stochastic process were introduced respectively. The dose-response data of 8 cases were constructed by different parameter settings. The simulated data were generated by R software and simulated by two non-parametric methods introduced. The difference between the BMD estimation value and the real value and the coverage of BMDL were taken as the evaluation index. Compare the performance of the two methods. Nine groups of cancer data were selected from the literature and estimated by two non-parametric methods and nine parameter models in BMDS software. Results: the estimation results of two nonparametric methods, NPB1 and NPB2, are close to the real BMD values. When the multistage model parameters were set to MS0 / 1 / 1 / 1 / 3 / 3, the dose group number was 6, and the dose spacing was logarithmic interval, the BMDL coverage of the method was significantly lower than that of the normal level BMD estimation. The results showed that the BMDL coverage of the NPB1 method was significantly lower than that of the normal level (P < 0.05). The larger bias may be caused by the over-fitting caused by relatively high posterior width parameters, and the RMSE coverage of both methods is closer to the true level than that of the RMSE value of the two methods, and the conservative method is more preferable than the NPB1 method. Compared with the results of different parametric models, the BMD estimates obtained by the two nonparametric methods fall into or are very close to the range of the BMD estimators obtained by the parametric models, but in the confidence interval, Most of the BMDLs values estimated by the nonparametric method are smaller than the BMDLs values calculated by the parametric model. Conclusion: the two non-parametric Bayesian methods can provide reasonable fitting values in the process of baseline dose estimation, especially when the traditional parametric model can not provide reasonable fitting values, and can also be applied very well. The NPB2 method is slightly better than NPB1 in estimating results and computing speed of software. Non-parametric method is very flexible and widely used in the process of model fitting. It is a very useful method in the future study of baseline dose estimation.
【学位授予单位】：山西医科大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R181.2

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