Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.
Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.
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缺失数据很重要：对比较有效性研究中缺失 EHR 数据的影响进行实证评估。
DOI：
10.1093/jamia/ocad066
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Huang,Jing
中科院分区：
文献类型：
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作者：
Zhou,Yizhao;Shi,Jiasheng;Stein,Ronen;Liu,Xiaokang;Baldassano,RobertN;Forrest,ChristopherB;Chen,Yong;Huang,Jing
在使用电子健康记录（EHR）的比较有效性研究（CER）中，缺失数据的影响可能因缺失数据的类型和模式而异。在这项研究中，我们的目的是量化这些影响，并比较不同的插补methods.Materials和MethodsWe的性能进行了实证（模拟）研究，以量化的偏差和功率损失估计治疗效果在CER使用EHR数据。我们考虑了各种缺失情况，并使用倾向评分来控制混杂因素。我们比较了多重插补和样条平滑方法来处理missing data.ResultsWhen缺失的数据依赖于疾病和医疗实践模式的随机进展的性能，样条平滑方法产生的结果是接近那些没有缺失的数据时获得的。与多重插补相比，样条平滑通常表现相似或更好，估计偏倚更小，功效损失更小。多重插补仍然可以减少研究偏倚和功率损失在一些限制性的情况下，例如，当缺失的数据并不依赖于疾病progress.Discussion和ConclusionMissing的随机过程中EHRs的数据可能会导致治疗效果和CER中的假阴性结果的偏倚估计，即使在缺失的数据进行插补。当使用EHR作为CER的数据来源时，重要的是利用疾病轨迹的时间信息来插补缺失值，并且在选择插补方法时考虑缺失率和效应量。
ObjectivesThe impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods.Materials and MethodsWe conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data.ResultsWhen missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression.Discussion and ConclusionMissing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.