Analytical Methods for a Learning Health System: 3. Analysis of Observational Studies.

Michael Stoto, Michael Oakes, Elizabeth Stuart, Randall Brown, Jelena Zurovac, Elisa L Priest
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引用次数: 7

Abstract

The third paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how analytical methods for individual-level electronic health data EHD, including regression approaches, interrupted time series (ITS) analyses, instrumental variables, and propensity score methods, can also be used to address the question of whether the intervention "works." The two major potential sources of bias in non-experimental studies of health care interventions are that the treatment groups compared do not have the same probability of treatment or exposure and the potential for confounding by unmeasured covariates. Although very different, the approaches presented in this chapter are all based on assumptions about data, causal relationships, and biases. For instance, regression approaches assume that the relationship between the treatment, outcome, and other variables is properly specified, all of the variables are available for analysis (i.e., no unobserved confounders) and measured without error, and that the error term is independent and identically distributed. The instrumental variables approach requires identifying an instrument that is related to the assignment of treatment but otherwise has no direct on the outcome. Propensity score methods approaches, on the other hand, assume that there are no unobserved confounders. The epidemiological designs discussed also make assumptions, for instance that individuals can serve as their own control. To properly address these assumptions, analysts should conduct sensitivity analyses within the assumptions of each method to assess the potential impact of what cannot be observed. Researchers also should analyze the same data with different analytical approaches that make alternative assumptions, and to apply the same methods to different data sets. Finally, different analytical methods, each subject to different biases, should be used in combination and together with different designs, to limit the potential for bias in the final results.

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学习型卫生系统的分析方法:3。观察性研究分析。
这是关于学习型健康系统如何使用常规收集的电子健康数据(EHD)来推进知识和支持持续学习的系列文章中的第三篇,本文描述了个人层面电子健康数据EHD的分析方法,包括回归方法、中断时间序列(ITS)分析、工具变量和倾向评分方法,也可以用来解决干预是否“有效”的问题。在卫生保健干预措施的非实验研究中,两个主要的潜在偏倚来源是,比较的治疗组不具有相同的治疗或暴露概率,以及被未测量的协变量混淆的可能性。尽管非常不同,本章中提出的方法都是基于对数据、因果关系和偏差的假设。例如,回归方法假设治疗、结果和其他变量之间的关系是适当指定的,所有变量都可用于分析(即没有未观察到的混杂因素),并且测量没有误差,并且误差项是独立的和均匀分布的。工具变量方法需要确定一种与治疗分配相关但对结果没有直接影响的工具。另一方面,倾向评分方法假设没有未观察到的混杂因素。所讨论的流行病学设计也做了假设,例如,个人可以作为他们自己的对照。为了正确处理这些假设,分析人员应该在每种方法的假设范围内进行敏感性分析,以评估无法观察到的潜在影响。研究人员还应该用不同的分析方法来分析相同的数据,这些方法可以做出不同的假设,并将相同的方法应用于不同的数据集。最后,不同的分析方法,每一种都有不同的偏差,应该与不同的设计结合使用,以限制最终结果中的偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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