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首页> 外文期刊>Statistics in medicine >How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS.
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How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS.

机译：模糊有多模糊？使用WinBUGS在MCMC中使用模糊先验分布的影响的模拟研究。

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There has been a recent growth in the use of Bayesian methods in medical research. The main reasons for this are the development of computer intensive simulation based methods such as Markov chain Monte Carlo (MCMC), increases in computing power and the introduction of powerful software such as WinBUGS. This has enabled increasingly complex models to be fitted. The ability to fit these complex models has led to MCMC methods being used as a convenient tool by frequentists, who may have no desire to be fully Bayesian.Often researchers want 'the data to dominate' when there is no prior information and thus attempt to use vague prior distributions. However, with small amounts of data the use of vague priors can be problematic. The results are potentially sensitive to the choice of prior distribution. In general there are fewer problems with location parameters. The main problem is with scale parameters. With scale parameters, not only does one have to decide the distributional form of the prior distribution, but also whether to put the prior distribution on the variance, standard deviation or precision.We have conducted a simulation study comparing the effects of 13 different prior distributions for the scale parameter on simulated random effects meta-analysis data. We varied the number of studies (5, 10 and 30) and compared three different between-study variances to give nine different simulation scenarios. One thousand data sets were generated for each scenario and each data set was analysed using the 13 different prior distributions. The frequentist properties of bias and coverage were investigated for the between-study variance and the effect size.The choice of prior distribution was crucial when there were just five studies. There was a large variation in the estimates of the between-study variance for the 13 different prior distributions. With a large number of studies the choice of prior distribution was less important. The effect size estimated was not biased, but the precision with which itwas estimated varied with the choice of prior distribution leading to varying coverage intervals and, potentially, to different statistical inferences. Again there was less of a problem with a larger number of studies. There is a particular problem if the between-study variance is close to the boundary at zero, as MCMC results tend to produce upwardly biased estimates of the between-study variance, particularly if inferences are based on the posterior mean.The choice of 'vague' prior distribution can lead to a marked variation in results, particularly in small studies. Sensitivity to the choice of prior distribution should always be assessed.
机译：在医学研究中，贝叶斯方法的使用最近有所增长。造成这种情况的主要原因是开发了基于计算机密集型仿真的方法，例如Markov链蒙特卡洛（MCMC），计算能力提高以及功能强大的软件（例如WinBUGS）的引入。这使得能够安装越来越复杂的模型。拟合这些复杂模型的能力导致常问者将MCMC方法用作便捷工具，他们可能不想完全使用贝叶斯方法。研究人员通常希望在没有先验信息的情况下以“数据为主导”，因此试图使用模糊的先验分布。但是，由于数据量少，使用模糊的先验可能会带来问题。结果可能对先验分布的选择敏感。通常，位置参数的问题较少。主要问题是比例参数。利用比例参数，不仅需要确定先验分布的分布形式，而且还要决定是否将先验分布放在方差，标准差或精度上。我们进行了模拟研究，比较了13种不同先验分布的影响用于模拟随机效应荟萃分析数据的比例参数。我们改变了研究的数量（5、10和30），并比较了三种不同的研究之间的方差，从而给出了九种不同的模拟方案。每个方案生成了1000个数据集，并使用13种不同的先验分布对每个数据集进行了分析。对于研究之间的方差和效应大小，研究了偏倚和覆盖的频繁性特征。只有五个研究时，选择先验分布至关重要。 13种不同的先验分布的研究之间方差的估计值存在很大差异。在大量研究中，选择先验分布不太重要。估计的效应大小没有偏倚，但是估计的精度随先验分布的选择而变化，从而导致覆盖间隔不同，并可能导致不同的统计推断。同样，大量的研究也减少了问题。如果研究之间的方差接近于零处的边界，则存在一个特殊的问题，因为MCMC结果倾向于产生研究之间的方差的向上偏差估计，尤其是如果推论是基于后验均值的话。先验分布会导致结果显着变化，尤其是在小型研究中。应始终评估对选择先前分配的敏感性。

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