A reply to Andrew Gelman's latest post where he links to an old post on propensity scores:
My understanding of the issue is that there was also a prevalent user problem (creating selection bias) at least partially due to time-varying risk. While this could have been found and modeled, I am unsure about how propensity scores give any advantage over a thoughtfully constructed regression model. Unless the study you are thinking of had a lot more power to estimate predictors of exposure than outcomes due to very few outcomes (but I don't believe that this was the case with the Nurse's Health Study).
I'm not saying that better statistical models shouldn't be used but I worry about overstating the benefits of propensity score analysis. It's an extremely good technique, no question about it, and I've published on one of it's variations. But I want to be very sure that we don't miss issues of study design and bias in the process.
Issues of self-selection seriously limit all observational epidemiology. The issue is serious enough that I often wonder if we should not use observational studies to estimate medication benefits (at all). It's just too misleading.
This minor point of disagreement aside, I freely admit that Andrew Gelman is one of my heroes in the statistical community. I love some of his posts. His work on statistical significance is incredibly thought provoking, very helpful in clarifying thought and a must read for any epidemiologist.,