Li Lihua, Cuerden Meaghan S, Liu Bian, Shariff Salimah, Jain Arsh K, Mazumdar Madhu
Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Risk Manag Healthc Policy. 2021 Feb 22;14:757-770. doi: 10.2147/RMHP.S275831. eCollection 2021.
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.
评估干预措施影响的统计方法在临床研究中越来越常用。然而，尚未有针对从业者的这些方法的全面综述。
我们对三种评估干预措施影响的方法进行了全面综述：差分法（DID）、中断时间序列的分段回归（ITS）和干预自回归积分移动平均法（ARIMA）。我们还对这些方法进行了比较，并通过三个重要的医疗保健相关应用展示了它们的使用方法。
在第一个例子中，与医疗补助扩展前时期相比，医疗补助扩展后时期扩展州和未扩展州之间医疗保险覆盖率差异的DID估计值为5.93（95%置信区间，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，在引入临床决策支持工具后，三级医院急诊科的平均影像检查医嘱适宜性评分超过了住院部，水平变化差异为0.63（95%置信区间，0.53至0.73），趋势变化差异为0.02（95%置信区间，0.01至0.03）。在第三个例子中，干预ARIMA分析的结果表明，在估算肾小球滤过率（eGFR）报告开始后的几个月内，肌酐清除率测试次数显著减少，下降幅度为每10万名成年人-0.93（95%置信区间，-1.22至-0.64）次测试，下降速率为每10万名成年人每月0.97（95%置信区间，0.95至0.99）次测试。
在选择合适的方法来模拟干预效果时，有必要考虑数据结构、研究设计、是否有合适的对照组、样本量要求、研究期间是否发生其他干预以及数据模式。
