Wednesday, January 14, 2015

A Credibility Revolution in Wellness Program Analysis?

Last november there was a post on the Health Affairs blog related to the evaluation of wellness programs. Here are some tidbits:

"This blog post will consider the results of two compelling study designs — population-based wellness-sensitive medical event analysis, and randomized controlled trials (RCTs). Then it will look at the popular, although weaker, participant vs. non-participant study design."

"More often than not wellness studies simply compare participants to “matched” non-participants or compare a subset of participants (typically high-risk individuals) to themselves over time."

“Looking at how participants improve versus non-participants…ignores self-selection bias. Self-improvers are likely to be drawn to self-improvement programs, and self-improvers are more likely to improve.” Further, passive non-participants can be tracked all the way through the study since they cannot “drop out” from not participating, but dropouts from the participant group—whose results would presumably be unfavorable—are not counted and are considered lost to follow-up. So the study design is undermined by two major limitations, both of which would tend to overstate savings."

Does Wellness need a credibility revolution?

These criticisms are certainly valid, however, my thoughts are that panel methodsdifference-in-difference and propensity score matching fit firmly in the Rubin Causal Model or potential outcomes framework for addressing issues related to selection bias. And what about examples of more robust quasi experimental approaches (like instrumental variables)?   These are all methods that are meant to deal specifically with the drawbacks of self comparisons and the issues mentioned above by the authors, and are at the heart of techniques related to the credibility revolution in econometrics.

A RCT is by far the most reliable way to identify treatment effects, but I know when it comes to applied work, RCT just isn't happening for a lot of obvious reasons. As Marc Bellemare might say, let the credibility revolution flow through you!

***this post was revised July 22, 2016, originally titled "Are Quasi-Experimental Designs Off the Table in Wellness Program Analysis"

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