Linden Consulting Group, LCC Ann Arbor, MI, USA.
BMC Med Res Methodol. 2013 Sep 28;13:119. doi: 10.1186/1471-2288-13-119.
Interventions targeting individuals classified as "high-risk" have become common-place in health care. High-risk may represent outlier values on utilization, cost, or clinical measures. Typically, such individuals are invited to participate in an intervention intended to reduce their level of risk, and after a period of time, a follow-up measurement is taken. However, individuals initially identified by their outlier values will likely have lower values on re-measurement in the absence of an intervention. This statistical phenomenon is known as "regression to the mean" (RTM) and often leads to an inaccurate conclusion that the intervention caused the effect. Concerns about RTM are rarely raised in connection with most health care interventions, and it is uncommon to find evaluators who estimate its effect. This may be due to lack of awareness, cognitive biases that may cause people to systematically misinterpret RTM effects by creating (erroneous) explanations to account for it, or by design.
In this paper, the author fully describes the RTM phenomenon, and tests the accuracy of the traditional approach in calculating RTM assuming normality, using normally distributed data from a Monte Carlo simulation and skewed data from a control group in a pre-post evaluation of a health intervention. Confidence intervals are generated around the traditional RTM calculation to provide more insight into the potential magnitude of the bias introduced by RTM. Finally, suggestions are offered for designing interventions and evaluations to mitigate the effects of RTM.
On multivariate normal data, the calculated RTM estimates are identical to true estimates. As expected, when using skewed data the calculated method underestimated the true RTM effect. Confidence intervals provide helpful guidance on the magnitude of the RTM effect.
Decision-makers should always consider RTM to be a viable explanation of the observed change in an outcome in a pre-post study, and evaluators of health care initiatives should always take the appropriate steps to estimate the magnitude of the effect and control for it when possible. Regardless of the cause, failure to address RTM may result in wasteful pursuit of ineffective interventions, both at the organizational level and at the policy level.
针对被归类为“高风险”的个体的干预措施已在医疗保健中变得很常见。高风险可能代表利用、成本或临床措施的异常值。通常，此类个体被邀请参与旨在降低其风险水平的干预措施，并且在一段时间后，会进行后续测量。然而，在没有干预的情况下，最初因其异常值而被识别的个体在重新测量时可能会有较低的值。这种统计现象被称为“均值回归”（RTM），并且经常导致不准确的结论，即干预导致了效果。与大多数医疗保健干预措施相关联，很少有人提出对 RTM 的担忧，并且很少有评估者估计其效果。这可能是由于缺乏认识，认知偏差可能导致人们通过创建（错误）解释来系统地误解 RTM 效果，或者是由于设计。
在本文中，作者充分描述了 RTM 现象，并使用来自蒙特卡罗模拟的正态分布数据和健康干预前后评估的对照组中的偏态数据，测试了在假设正态性的情况下传统方法计算 RTM 的准确性。置信区间围绕传统的 RTM 计算生成，以更深入地了解 RTM 引入的偏差的潜在幅度。最后，提出了设计干预措施和评估措施以减轻 RTM 影响的建议。
在多元正态数据上，计算出的 RTM 估计值与真实估计值相同。正如预期的那样，当使用偏态数据时，计算方法低估了真实的 RTM 效果。置信区间为 RTM 效果的幅度提供了有帮助的指导。
决策者应始终将 RTM 视为前后研究中观察到的结果变化的一种可行解释，并且医疗保健计划的评估者应始终采取适当的步骤来估计效果的幅度，并在可能的情况下进行控制。无论原因如何，未能解决 RTM 可能会导致在组织和政策层面上浪费地追求无效的干预措施。
