Department of Statistical Science, Baylor University, Waco, TX, USA.
CSL Behring, CSL Limited, King of Prussia, PA, USA.
BMC Med Res Methodol. 2024 Sep 7;24(1):195. doi: 10.1186/s12874-024-02322-2.
The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families.
无法正确解释未测量的混杂因素会导致参数估计的偏差、无效的不确定性评估和错误的结论。敏感性分析是一种用于研究观察性研究中未测量混杂因素影响的方法。然而，由于缺乏可访问的软件，这种方法的采用一直很慢。对现有的用于解决未测量混杂因素的 R 包进行了广泛的审查，列出了确定性敏感性分析方法，但没有列出用于概率敏感性分析的 R 包。R 包 unmconf 通过贝叶斯未测量混杂模型实现了第一个用于概率敏感性分析的可用包。该包允许正态、二项、泊松或伽马响应，从正态或二项分布中考虑一个或两个未测量的混杂因素。unmconf 的目标是实现一个用户友好的包，在存在未测量混杂因素的情况下执行贝叶斯建模，前端有简单的命令，后端执行更密集的计算。我们通过新的模拟研究来研究这个包的适用性。结果表明，当在不同的响应-未测量混杂因素分布家族组合中对不同程度的内部/外部验证数据进行建模时，可信区间将具有接近名义覆盖率和较小的偏差。
