Decision analysis? – where are the details?

November 15, 2015


In the Milan conference (1st EFLM Strategic Conference Defining analytical performance goals) one of the papers (1) suggests that analytical performance specifications should be prepared from indirect outcome studies using decision analysis. The only example presented is a simulation, which is not decision analysis. Decision analysis is also discussed in this section but on an abstract level.

I have performed decision analysis and discuss it in my book (2). Decision analysis requires a quantitative variable that is either maximized or minimized. In my case, we performed financial decision analysis and the parameter to be maximized was net present value (NPV) of future cash flows. The Milan paper never identifies a quantitative parameter to be optimized.

I don’t understand how decision analysis can be recommended without any known examples or details about how one would go about it.


  1. Horvath AR, Bossuyt PMM, Sandberg S, Setting analytical performance specifications based on outcome studies – is it possible? Clin Chem Lab Med 2015; 53(6): 841–848.
  2. Assay Development and Evaluation: A Manufacturer’s Perspective. Jan S. Krouwer, AACC Press, Washington DC, 2002, see Chapter 3.

How to insult clinicians

November 5, 2015


In my blog post Total Error and Milan, I mentioned how clinician surveys were in the draft consensus statement but dropped from the final and published consensus statement. (The draft consensus statement is no longer available on the EFLM site).

I had occasion to read a paper from the Milan conference (1) where it is clear why clinician surveys were dropped.

“RCVs from vignettes should probably not be used on their own as a basis for setting analytical performance specifications, since clinicians seem “uninformed” regarding important principles.”

RCV = reference change values.

For an example of how clinician surveys were used to set analytical performance specifications, see reference 2.


  1. Thue G and Sandberg S: Analytical performance specifications based on how clinicians use laboratory tests Clin Chem Lab Med 2015; 53(6): 857–862.
  2. Klonoff DC, Lias C, Vigersky R, et al The surveillance error grid. J Diabetes Sci Technol. 2014;8:658-672.