PURPOSE: We systematically reviewed pharmacoepidemiologic and comparative effectiveness studies that use probabilistic bias analysis to quantify the effects of systematic error including confounding, misclassification, and selection bias on study results.METHODS: We found articles published between 2010 and October 2015 through a citation search using Web of Science and Google Scholar and a keyword search using PubMed and Scopus. Eligibility of studies was assessed by one reviewer. Three reviewers independently abstracted data from eligible studies.RESULTS: Fifteen studies used probabilistic bias analysis and were eligible for data abstraction-nine simulated an unmeasured confounder and six simulated misclassification. The majority of studies simulating an unmeasured confounder did not specify the range of plausible estimates for the bias parameters. Studies simulating misclassification were in general clearer when reporting the plausible distribution of bias parameters. Regardless of the bias simulated, the probability distributions assigned to bias parameters, number of simulated iterations, sensitivity analyses, and diagnostics were not discussed in the majority of studies.CONCLUSION: Despite the prevalence and concern of bias in pharmacoepidemiologic and comparative effectiveness studies, probabilistic bias analysis to quantitatively model the effect of bias was not widely used. The quality of reporting and use of this technique varied and was often unclear. Further discussion and dissemination of the technique are warranted.
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机译：目的：我们系统地回顾了药物流行病学和比较有效性研究，该研究使用概率偏倚分析来量化系统误差（包括混淆，错误分类和选择偏倚）对研究结果的影响。方法：我们通过使用Web of Science和Google Scholar，以及使用PubMed和Scopus进行关键字搜索。一名审阅者评估了研究的资格。三名审稿人从符合条件的研究中独立提取数据。结果：十五项研究使用概率偏差分析并符合数据抽象的资格-九项模拟未测混杂因素，六项模拟错误分类。模拟未测混杂因素的大多数研究都没有指定偏差参数的合理估计范围。当报告偏差参数的合理分布时，模拟错误分类的研究通常更为清晰。不管模拟的偏倚如何，分配给偏倚参数的概率分布，模拟的迭代次数，敏感性分析和诊断都没有在大多数研究中进行讨论。偏倚分析用于定量模拟偏倚的影响并未得到广泛使用。报告和使用此技术的质量各不相同，并且通常不清楚。有必要对该技术进行进一步的讨论和传播。
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