Three Statistical Approaches for Assessment of Intervention Effects: A Primer for Practitioners.
Three Statistical Approaches for Assessment of Intervention Effects: A Primer for Practitioners.
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DOI：
10.2147/rmhp.s275831
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发表时间：
2021
影响因子：
3.5
通讯作者：
Mazumdar M
中科院分区：
文献类型：
--
作者：
Li L;Cuerden MS;Liu B;Shariff S;Jain AK;Mazumdar M
评估干预措施影响的统计方法越来越多地用于临床研究环境。然而，一个面向从业人员的方法的全面审查尚未提供。我们提供了一个全面的审查三种方法来评估干预的影响：差异中的差异（DID），分段回归中断时间序列（ITS），和干预自回归综合移动平均（ARIMA）。我们还比较了这些方法，并通过三个重要的医疗保健相关应用程序来说明它们的使用。在第一个例子中，与扩展前相比，在医疗补助扩展后，扩展州和未扩展州之间的医疗保险覆盖率差异的DID估计为5.93（95%CI，3.99至7.89）个百分点。在第二个例子中，ITS分析的比较分段回归显示，三级护理医院急诊科的平均成像顺序适当性得分超过住院设置的平均成像顺序适当性得分，水平变化差异为0.63（95% CI，0.53至0.73），引入临床决策支持工具后的趋势变化差异为0.02（95% CI，0.01至0.03）。在第三个例子中，来自干预性ARIMA分析的结果显示，在eGFR报告开始后的几个月内，肌酐清除率测试的数量显著下降，下降幅度等于-0.93（95%CI，-1.22至0.64）测试每100，000名成人和下降率等于0.97（95%CI，0.95至0.99）测试每100，000名成人每月。当选择适当的方法来模拟干预效应时，有必要考虑数据结构、研究设计、适当比较组的可用性、样本量要求、研究窗口期间是否发生其他干预以及数据模式。
Statistical methods to assess the impact of an intervention are increasingly used in clinical research settings. However, a comprehensive review of the methods geared toward practitioners is not yet available. We provide a comprehensive review of three methods to assess the impact of an intervention: difference-in-differences (DID), segmented regression of interrupted time series (ITS), and interventional autoregressive integrated moving average (ARIMA). We also compare the methods, and provide illustration of their use through three important healthcare-related applications. In the first example, the DID estimate of the difference in health insurance coverage rates between expanded states and unexpanded states in the post-Medicaid expansion period compared to the pre-expansion period was 5.93 (95% CI, 3.99 to 7.89) percentage points. In the second example, a comparative segmented regression of ITS analysis showed that the mean imaging order appropriateness score in the emergency department at a tertiary care hospital exceeded that of the inpatient setting with a level change difference of 0.63 (95% CI, 0.53 to 0.73) and a trend change difference of 0.02 (95% CI, 0.01 to 0.03) after the introduction of a clinical decision support tool. In the third example, the results from an interventional ARIMA analysis show that numbers of creatinine clearance tests decreased significantly within months of the start of eGFR reporting, with a magnitude of drop equal to −0.93 (95% CI, −1.22 to −0.64) tests per 100,000 adults and a rate of drop equal to 0.97 (95% CI, 0.95 to 0.99) tests per 100,000 per adults per month. When choosing the appropriate method to model the intervention effect, it is necessary to consider the structure of the data, the study design, availability of an appropriate comparison group, sample size requirements, whether other interventions occur during the study window, and patterns in the data.