Assessing regression to the mean effects in health care initiatives

BMC Medical Research Methodology

Ariel Linden¹ 

[1] Linden Consulting Group, LCC Ann Arbor, MI, USA

关键词: Simulation;    Confidence intervals;    Outcomes;    Validity;    Outliers;    Regression to the mean;   

Others  :  1091617
DOI  :  10.1186/1471-2288-13-119

 received in 2013-07-02, accepted in 2013-09-20,  发布年份 2013

【 摘 要 】

Background

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.

Methods

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.

Results

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.

Conclusion

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.

【 授权许可】

   
2013 Linden; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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