Anchored on Anchoring: A Concept Cut from Whole Cloth

Welcome back to the blog. An article published today in JAMA Internal Medicine was just the impetus I needed to return after more than a year.

Hardly a student of medicine who has trained in the past 10 years has not heard of "anchoring bias" or anchoring on a diagnosis. What is this anchoring? Customarily in cognitive psychology, to demonstrate a bias empirically, you design an experiment that shows directional bias in some response, often by introducing an irrelevant (independent) variable e.g., a reference frame, as we did here and here. Alternatively, you could show bias if responses deviate from some known truth value as we did here. What does not pass muster is to simply say "I think there is a bias whereby..." and write an essay about it.

That is what happened 20 years ago when an expository essay by Crosskerry proposed "anchoring" as a bias in medical decision making, which he ostensibly named after the "anchoring and adjustment" heuristic demonstrated by Kahneman and Tversky (K&T) in  experiments published in their landmark 1974 Science paper. The contrast between "anchoring to a diagnosis" (A2D) and K&T's anchoring and adjustment (A&A) makes it clear why I bridle so much at the former. 

To wit: First, K&T showed A&A via an experiment with an independent (and irrelevant) variable. They had participants in this experiment spin a dial on a wheel with associated numbers, like on the Wheel of Fortune game show. (They did not know that the dial was rigged to land on either 10 or 65.) They were then asked whether the number of African countries that are members of the United Nations was more or less than that number; and then to give their estimate of the number of member countries. The numerical anchors, 10 and 65, biased responses. For the group of participants whose dials landed on 10, their estimates were lower, and for the other group (65), they were higher. 

Just Do As I (Vaguely) Say: The Folly of Clinical Practice Guidelines

If you didn't care to know anything about finance, and you hired a financial adviser (paid hourly, not through commissions, of course) you would be happy to have him simply tell you to invest all of your assets into a Vanguard life cycle fund.  But you may then be surprised that a different adviser told one of your contemporaries that the approach was oversimple and that you should have several classes of assets in your portfolio that are not included in the life cycle funds, such as gold or commodities.  In light of the discrepancies, you may conclude that to make the best economic choices for yourself, you need to understand finance and the data upon which the advisers are basing their recommendations.

Making medical decisions optimally is akin to making economic decisions and is founded on a simple framework:  EUT, or Expected Utility Theory.  To determine whether to pursue a course of action versus another one, we add up the benefits of a course multiplied by their probability of accruing (that product is the positive utility of the course of action) and then subtract the product of the costs of the course of action and their probability of accruing (the negative utility).  If utility is positive, we pursue a course of action, and if options are available, we pursue the course with the highest positive utility.  Ideally, anybody helping you navigate such a decision framework would tell you the numbers so you could do the calculus.  Using the finance analogy again, if the adviser told you "Stocks have positive returns.  So do bonds.  Stocks are riskier than bonds" - without any quantification, you may conclude that a portfolio full of bonds is the best course of action - and usually it is not.

I regret to report that that is exactly what clinical practice guideline writers do:  provide summary information without any numerical data to support it, leaving the practitioner with two choices:

1. Just do as the guideline writer says
2. Go figure it out for herself with a primary data search