CMEPalooza

On Tuesday, Chicago will decide on either Rahm on Chuy.  But Wednesday, it’s all about CMEPalooza.  Thank you to our industry’s “Jane’s Addiction” for organizing the third installment of this CME free-for-all.  I’ll be presenting on CME outcomes assessment (11 AM eastern). My session is designed for those that fall into the following categories:

  • Regularly use surveys to measure learning and competence change
  • No formal process for reviewing survey questions
  • Unsure of how to utilize statistical tests

Oh, but there’s more…this session has been accredited by the apocryphal League of CME Assessors (sorry, can’t provide a link due to exclusivity).  If, after completing the session, you wish to be considered for eligibility as “CME Outome Statistician, first grade”, click here (sorry, this test is now closed) to take their test. There’s even a certificate if you pass. Good luck!

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2 Comments

Filed under CMEpalooza

2 responses to “CMEPalooza

  1. Jason, thank you for your presentation on CMEPalooza today, and for covering how to choose the appropriate statistical test for categorical and ordinal data. I use Fisher’s Exact Test rather than Chi Square even for larger samples because it “always gives an exact P value” according to the guidance at GraphPad.com (see http://graphpad.com/quickcalcs/contingency1/). GraphPad even says, “Only choose chi-square if someone requires you to.”
    Do you think that our readers would think that using Fisher’s exact test indicates that we were forced to use it because of small samples … even if we have enough samples?
    Thank you,
    Sandra Binford

    P.S. Here’s the full text from that page: “There are three ways to compute a P value from a contingency table. Fisher’s test is the best choice as it always gives the exact P value, while the chi-square test only calculates an approximate P value. Only choose chi-square if someone requires you to. The Yates’ continuity correction is designed to make the chi-square approximation better. With large sample sizes, the Yates’ correction makes little difference. With small sample sizes, chi-square is not accurate, with or without the correction.”

    • assesscme

      Hi Sandra, in short…yes, I’m on board with using Fisher’s for the reasons you specify. But, I wouldn’t discourage anyone in CME from using chi-square in that the added precision of Fisher’s may not be applicable to CME. If I was running a clinical trial, I’d be worried about such precision in that small margins of difference could have big implications for patient health. However, I think applying statistical tests of significance to small changes in knowledge or competence is more of a concern than whether the resulting p value is exact or an approximation. That is, such small changes aren’t likely meaningful – regardless of their p value.

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