Regression to the Mean: A Commonly Overlooked and Misunderstood Factor Leading to Unjustified Conclusions in Pediatric Obesity Research.

Abstract

In this paper we discuss what regression to the mean (RTM) is, the magnitude of RTM in realistic situations, interpretation of RTM, and recommendations for how to address RTM in study design. Public health research faces many challenges in conducting gold standard randomized, controlled trials (RCT). Although there are many threats to validity in uncontrolled trials, RTM is often overlooked or not adequately considered. RTM is a statistical phenomenon that occurs with any pair of variables that have a correlation not equal to |1.0|. With RTM, subjects' average values on an outcome variable (e.g., BMI) change in a systematic direction over time despite there being no treatment effect. Without a proper control group, changes thought to be associated with an intervention may be due entirely to RTM. Investigators may draw erroneous conclusions based on results showing greater declines in a variable among participants with higher baseline of that variable compared to those with lower baseline of that variable, and label this evidence for differential treatment efficacy. Ignoring RTM can lead to unsubstantiated conclusions about the effects of treatments. These conclusions can lead to the waste of time, money, and other resources, which distract from finding appropriate interventions. When a true RCT design is not feasible, reasonable design alternatives involving nonrandomized control groups should be implemented.