Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland.
Stat Med. 2010 May 30;29(12):1325-39. doi: 10.1002/sim.3858.
For bivariate meta-analysis of diagnostic studies, likelihood approaches are very popular. However, they often run into numerical problems with possible non-convergence. In addition, the construction of confidence intervals is controversial. Bayesian methods based on Markov chain Monte Carlo (MCMC) sampling could be used, but are often difficult to implement, and require long running times and diagnostic convergence checks. Recently, a new Bayesian deterministic inference approach for latent Gaussian models using integrated nested Laplace approximations (INLA) has been proposed. With this approach MCMC sampling becomes redundant as the posterior marginal distributions are directly and accurately approximated. By means of a real data set we investigate the influence of the prior information provided and compare the results obtained by INLA, MCMC, and the maximum likelihood procedure SAS PROC NLMIXED. Using a simulation study we further extend the comparison of INLA and SAS PROC NLMIXED by assessing their performance in terms of bias, mean-squared error, coverage probability, and convergence rate. The results indicate that INLA is more stable and gives generally better coverage probabilities for the pooled estimates and less biased estimates of variance parameters. The user-friendliness of INLA is demonstrated by documented R-code.
对于诊断研究的双变量荟萃分析，似然方法非常流行。然而，它们经常遇到可能不收敛的数值问题。此外，置信区间的构建也存在争议。可以使用基于马尔可夫链蒙特卡罗 (MCMC) 抽样的贝叶斯方法，但通常难以实现，并且需要长时间运行和诊断收敛检查。最近，提出了一种使用集成嵌套拉普拉斯逼近 (INLA) 的潜在高斯模型的新贝叶斯确定性推断方法。通过这种方法，由于后验边际分布被直接和准确地逼近，因此 MCMC 抽样变得多余。通过一个真实数据集，我们研究了所提供的先验信息的影响，并比较了 INLA、MCMC 和最大似然程序 SAS PROC NLMIXED 获得的结果。通过模拟研究，我们进一步通过评估其偏差、均方误差、覆盖概率和收敛速度来比较 INLA 和 SAS PROC NLMIXED 的性能。结果表明，INLA 更稳定，对于汇总估计通常给出更好的覆盖概率，并且方差参数的估计偏差更小。通过记录的 R 代码展示了 INLA 的易用性。
