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Introduction
Regression to the Mean (RTM) is a statistical phenomenon where extreme values on one measurement tend to move closer to the average on subsequent measurements due to natural variability. In this Premium-exclulsive episode, Danny gives an explanation of this concept with some examples in nutrition research.
Related resources
- Join the Sigma newsletter for free
- Subscribe to Sigma Nutrition Premium
- References:
- [01:37]Common claims and narratives
- [03:15]Historical context of dietary guidelines
- [21:57]Sugar industry influence on dietary research
- Glucose Peaks
- Some Pragmatic Considerations
The Hosts
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Dr. Alan Flanagan has a PhD in nutrition from the University of Surrey, where his doctoral research focused on circadian rhythms, feeding, and chrononutrition.
This work was based on human intervention trials. He also has a Masters in Nutritional Medicine from the same institution.
Dr. Flanagan is a regular co-host of Sigma Nutrition Radio. He also produces written content for Sigma Nutrition, as part of his role as Research Communication Officer.
Danny Lennon has a master’s degree (MSc.) in Nutritional Sciences from University College Cork, and he is the founder of Sigma Nutrition.
Danny is currently a member of the Advisory Board of the Sports Nutrition Association, the global regulatory body responsible for the standardisation of best practice in the sports nutrition profession.
Quick Summary Notes: Regression to the Mean
Introduction to Regression to the Mean (RTM)
- Definition: RTM is a statistical phenomenon where extreme values on one measurement tend to move closer to the average on subsequent measurements due to natural variability.
- Origin: First discussed by Sir Francis Galton in 1877, who observed that extreme traits in parents (e.g., height) tended to regress towards the average in their children.
Importance in Nutrition Research
- Misinterpretation Risk: RTM can lead to false conclusions about the effectiveness of interventions.
- Examples in Studies:
- High initial blood pressure measurements may decrease naturally over time.
- Low initial HDL cholesterol levels may increase without intervention.
- Impact: RTM can significantly affect the interpretation of study results, making it crucial to account for this phenomenon in research designs.
Normal Distribution & Extreme Values
- Look at the graph below. It is a graphical example that illustrates the true mean and variation, as well as regression to the mean, using a normal distribution.
- Over the page, we will explain these concepts in more detail.
- This specific example (from Barrett et , 2005) represents the distribution of high-density lipoprotein (HDL) cholesterol levels in a single subject.
- X-Axis (HDL Cholesterol Levels in mg/dl): Shows the range of HDL cholesterol levels.
- Y-Axis (Frequency): Represents the frequency or probability of observing each HDL cholesterol level.
