HMD: Missing data in propensity score analyses of Electronic Health Records Data

常规收集的健康数据的电子存储和链接为解决重要问题提供了大量机会，尤其是与长期用药的可能危害和好处有关的问题。这些信息对患者和卫生保健专业人员都很重要。事实上，电子健康记录和相关数据将被用来衡量药物效果的期望现在被写入了欧盟立法。因此，我们预计用于研究的电子健康记录的使用将大幅增加。使用从电子健康记录中获取的数据来调查药物的影响带来了巨大的挑战。特别是，给患者开了特定药物的患者往往与没有开处方的患者非常不同。将这些患者的差异与药物的效果分开是观察流行病学的一个关键目标。即使在有关患者特征的信息(如他们的胆固醇水平或年龄)可用时，这种分离过程也是具有挑战性的，当一些信息不可用时，这一过程非常复杂。倾向得分分析是一种统计方法，它非常有用，可以解释开了药和没有开药的患者之间的不同患者特征，以衡量药物的效果。通过对药物处方过程的建模，倾向评分法试图识别开了药的患者和其他没有开药的患者在其他方面具有可比性，并通过比较这些患者之间的健康结果来衡量药物的效果。然而，在倾向评分分析中解释缺失患者信息的方法仍然缺乏理解。在药物效果调查中未能充分处理缺失信息可能导致关于药物益处或危害的错误结论。为了避免这种情况，至关重要的是制定适当的方法来处理倾向得分分析中的缺失信息。关于如何处理其他类型的分析中的缺失信息，特别是那些专注于将结果作为患者特征的函数进行建模的分析，已有既定的文献。然而，在这种结果建模方法和倾向评分分析中使用患者特征的方式在实际重要方面有所不同。因此，在结果回归建模的背景下，不能直接从我们的经验中学习处理缺失数据的方式。我们的建议旨在为进行这些分析的研究人员制定指南，以帮助他们选择适当的方法来处理他们丢失的数据，并理解他们关于药物效果的结论在哪些假设下是有效的。作为这项工作的一部分，我们将采用复杂的统计方法来处理在倾向得分设置之外证明自己的丢失数据，例如多重归因法，并以与倾向得分分析的目标和结构一致的方式开发和应用它们。由于特定患者的药物使用经常会随着时间的推移而变化，就像患者的许多特征一样，因此在统计分析中通常需要考虑这一点。这可以通过应用被称为边际结构模型的倾向评分法的扩展来实现。因此，我们建议的最后一个方面是试图了解如何将我们提出的缺失数据方法扩展到这种设置。通过我们基础广泛的传播战略(在其他地方介绍)，我们的工作将与学术、制药、监管和政策环境中医学和社会科学的广泛量化研究人员相关。

Electronic storage and linkage of routinely-collected health data has opened up substantial opportunities to address important questions, not least those relating to the possible harms and benefits of long term medication use. Such information is important to patients and health care professionals alike. Indeed, the expectation that EHR and related data will be used to measure medication effects is now written into EU legislation. Thus we expect the use of electronic health records for research will increase dramatically.Using data taken from electronic health records to investigate medication effects raises substantial challenges. In particular, patients who are prescribed a particular medication will tend to be very different from those who are not. Disentangling these patient differences from effects of the medication is a key aim of observational epidemiology. This process of disentangling, which is challenging even when information concerning patient characteristics (such as their cholesterol level or age) is available, is greatly complicated when some information is unavailable. A propensity score analysis is a statistical approach that is very useful in accounting for differing patient characteristics between patients prescribed a medication and those who are not, in order to measure effects of the medication. By modelling the process of medication prescription, propensity score methods attempt to identify patients prescribed the medication and others who are not prescribed the medication who are otherwise comparable, and measures effects of the medication by comparing health outcomes between these patients. Methods for accounting for missing patient information within a propensity score analysis, however, remain poorly understood.Failure to adequately handle missing information in an investigation of medication effects could lead to incorrect conclusions regarding the benefits, or harms, of the medication. In order to avoid this, it is vital to develop appropriate ways of dealing with missing information within propensity score analyses. There is an established literature concerning how to handle missing information within other types of analyses, particularly those focused around modelling the outcome as a function of the patient characteristics. However, the way in which the patient characteristics are used in this outcome modelling approach and propensity score analyses differs in practically important ways. Thus the way in which missing data should be handled cannot be directly learnt from our experiences within the outcome regression modelling context. Our proposal aims to develop guidelines for researchers undertaking these analyses to help them select an appropriate method for handling their missing data, and to understand the assumptions under which their conclusions regarding the effects of the medication are valid. As part of this, we will take sophisticated statistical methods for handling missing data that have proved themselves outside the propensity score setting, such as multiple imputation, and develop and apply them in a way that is consistent with the goals and structure of propensity score analyses.Because medication use of a particular patient will often change over time, as will many of the patient's characteristics, it is often desirable to take this into account in the statistical analysis. This can be done through the application of an extension of the propensity score approach, called marginal structural models. A final aspect of our proposal, therefore, seeks to understand how to extend our proposed missing data methods to this setting. Through our broad based dissemination strategy (described elsewhere) our work will be relevant to a broad range of quantitative researchers in medical and social science, in academic, pharmaceutical, regulatory and policy settings.