Adam, thank you for elucidating the importance of these basic epidemiological concepts in an easy to understand concepts. I just wish that your writing is picked up by the common media, even Fox News!
This was a good read. The appropriate communication of scientific results is hard. The execution of good science is hard. The proper interpretation and analysis of data is hard. I just wrote about how regression adjustments don’t completely control for confounding though they’re often treated that way. Data never speaks for itself.
Adam, thank you. Of course you are correct here. However, the opposite problem also occurs and it can cause huge problems. That is, researchers sometimes treat all manner of variables as confounders when they are in fact not valid confounders. Such faulty adjustment can cause a real effect to disappear from the results.
All scientists should know that a confounder is not just any variable that reduces the estimated association ("effect size") when it's included in a regression. A valid confounder needs to cause the outcome — formally, at least be independently associated with the outcome — and it must not be caused by the exposure.
Here is a real example. I am omitting key details. A research team observed an increasing rate of diagnoses of a medical condition. They suspected that the increase might be an artifact of a policy change rather than a true increase. So they adjusted for the policy change, comparing the diagnosis rates before and after the policy change. That made the increased diagnosis rate largely disappear. From that they concluded that the increased rate did not reflect reality. (They did not publish the unadjusted data, but they should have.) They did not cite any evidence that the policy changed caused an increasing rate of diagnosis. From then on, that team and their collaborators variously claimed or simply assumed that the observed increased rate of diagnosis was not real. That steered the course of investigation into the condition in the wrong direction.
The analysis that used faulty adjustment simply adjusted for a date (year). Any event that year would have produced exactly the same result. For example, adjustment for the year some of political election would lead to the false conclusion that there was no actual trend in the medical condition.
In sum, studies should show raw, unadjusted results as well as adjusted results, and they should justify the validity of each included confounder.
I've never understood the concept of the data speaking for themselves. Trump speaks for himself and it's complete gobbledegook.
Ha ha! Very good analogy 😁
Adam, thank you for elucidating the importance of these basic epidemiological concepts in an easy to understand concepts. I just wish that your writing is picked up by the common media, even Fox News!
This was a good read. The appropriate communication of scientific results is hard. The execution of good science is hard. The proper interpretation and analysis of data is hard. I just wrote about how regression adjustments don’t completely control for confounding though they’re often treated that way. Data never speaks for itself.
Adam, thank you. Of course you are correct here. However, the opposite problem also occurs and it can cause huge problems. That is, researchers sometimes treat all manner of variables as confounders when they are in fact not valid confounders. Such faulty adjustment can cause a real effect to disappear from the results.
All scientists should know that a confounder is not just any variable that reduces the estimated association ("effect size") when it's included in a regression. A valid confounder needs to cause the outcome — formally, at least be independently associated with the outcome — and it must not be caused by the exposure.
Here is a real example. I am omitting key details. A research team observed an increasing rate of diagnoses of a medical condition. They suspected that the increase might be an artifact of a policy change rather than a true increase. So they adjusted for the policy change, comparing the diagnosis rates before and after the policy change. That made the increased diagnosis rate largely disappear. From that they concluded that the increased rate did not reflect reality. (They did not publish the unadjusted data, but they should have.) They did not cite any evidence that the policy changed caused an increasing rate of diagnosis. From then on, that team and their collaborators variously claimed or simply assumed that the observed increased rate of diagnosis was not real. That steered the course of investigation into the condition in the wrong direction.
The analysis that used faulty adjustment simply adjusted for a date (year). Any event that year would have produced exactly the same result. For example, adjustment for the year some of political election would lead to the false conclusion that there was no actual trend in the medical condition.
In sum, studies should show raw, unadjusted results as well as adjusted results, and they should justify the validity of each included confounder.