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Confounding

Outcomes

Human behavior and outcomes are complex, and there are numerous mechanisms that could be affecting your outcomes.

Confounding

For example, suppose you want to compare the hospital outcomes of those who received medication in the prehospital setting to those who did not. You find that the outcomes for those who did not receive medication were actually better than the outcomes for those who did.  However, this does not take into account that those who received medication were likely to have a more severe illness in the first place. This situation is referred to as confounding.

Confounding is a mixing of effects.  It can make it look like there is relationship between two variables when there is none, or it can mask a true relationship. Mixing of Effects

Confounding is a mixing of effects.  It can make it look like there is relationship between two variables when there is none, or it can mask a true relationship.

Example:

Suppose you want to look at how your "Buckle Up" injury prevention program affected seat belt use.  You compare seat belt use before and after your program was administered. It appears that seat belt use has improved by 5% since the law was passed. However, during this same time period, a law was passed with primary enforcement for improper child safety or seat belt use. It's possible this factor was actually responsible for the increased seat belt use. The legislation passed is referred to here as a confounder. 

Common confounders include:

  • Population changes
  • Legislation
  • Media Awareness
  • Environmental Changes

 

 

rev. 29-Aug-2016

 

 

 

Some Common Confounders
NEDARC Can Help

Contact a NEDARC statistician for help on bias and confounding techniques.

 

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