Current Practices in Missing Data Handling for Interrupted Time Series Studies Performed on Individual-Level Data: A Scoping Review in Health Research.
Current Practices in Missing Data Handling for Interrupted Time Series Studies Performed on Individual-Level Data: A Scoping Review in Health Research.
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DOI：
10.2147/clep.s314020
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发表时间：
2021
影响因子：
3.9
通讯作者：
Petersen I
中科院分区：
文献类型：
--
作者：
Bazo-Alvarez JC;Morris TP;Carpenter JR;Petersen I
在中断时间序列（ITS）分析中，缺失数据会产生有偏估计。我们回顾了最近关于健康主题的ITS调查，以确定1）数据管理策略和进行的统计分析，2）考虑缺失数据的频率，以及如何评估，报告和处理。这是一次范围界定审查，遵循了PRISMA范围界定审查扩展的标准建议。我们纳入了2019年发表的所有ITS研究的随机样本，这些研究评估了与医疗保健相关的任何干预措施（例如，政策或计划），并在MEDLINE上索引了摘要。从732项研究中，我们最终回顾了60项。缺失数据的报告很少。数据汇总、人口一级数据建模的统计工具和完整的案例分析是首选，但当数据随机缺失时，这些可能导致偏差。季节性和其他时间依赖性混杂因素很少被考虑，当它们被考虑时，缺失数据的影响通常被忽略。很少有研究反映数据缺失的后果。与最佳实践相比，最近为健康研究进行的ITS研究中缺失数据的处理和报告有许多缺点。
Missing data can produce biased estimates in interrupted time series (ITS) analyses. We reviewed recent ITS investigations on health topics for determining 1) the data management strategies and statistical analysis performed, 2) how often missing data were considered and, if so, how they were evaluated, reported and handled. This was a scoping review following standard recommendations from the PRISMA Extension for Scoping Reviews. We included a random sample of all ITS studies that assessed any intervention relevant to health care (eg, policies or programmes) with individual-level data, published in 2019, with abstracts indexed on MEDLINE. From 732 studies identified, we finally reviewed 60. Reporting of missing data was rare. Data aggregation, statistical tools for modelling population-level data and complete case analyses were preferred, but these can lead to bias when data are missing at random. Seasonality and other time-dependent confounders were rarely accounted for and, when they were, missing data implications were typically ignored. Very few studies reflected on the consequences of missing data. Handling and reporting of missing data in recent ITS studies performed for health research have many shortcomings compared with best practice.