The p-value discussion started by an arcile authored by Tom Siegfried has generatesd a lot of discussion. Andrew Gelman has tried to round up many of the discussion points.
But the best part of the post (besides showing the diversity out there) was hidden at the bottom. Andrew comments:
"In all the settings I've ever worked on, the probability that the model is true is . . . zero!"
Well, he is most certainly correct in pharamcoepidemiology as well. I see a lot of variation over how to handle the biases that are inherent in observational pharmacoepidemiology -- but the focus on randomized drug trials should be a major clue that these associations are tricky to model. As a point of fact, the issue of confounding by indication, channeling bias, indication bias or whatever else you want to call it is central to the field. And the underlying idea here is that we can't get enough information about participants to model the influence of drugs being channeled to sicker patients.
So I wish that, in my field as well, people would realize that the relationships are tricky and no model is ever going to be absolutely correctly specified.
No comments:
Post a Comment