EBM 2.0

“EBM 1.0 was riddled with bugs, does not function and is no longer supported.

Continued use of EBM requires an update to EBM 2.0″ … Dr. Anand Senthi

My friend Dr Anand Senthi [ @DrSenthi  ] has spent countless hours delving into the statistical science that underpins our profession.  Evidence-based medicine is the science upon which we base our practice. It is the mechanism by which medicine has evolved in the latter half of the twentieth century and up until today. On his journey from “early-adopter” to “skeptic” Anand has posed an elementary question:  “does it work?”

I was lucky enough to catch a talk by Anand at an online “conference” entitled: “Evidence Based Fraud & the End of Statistical Significance”. It was a cracking talk that really got at the heart of our science and gave a vision for the future of medical research and practice.

EBM 2.0 is Anand’s proposed update to EBM as we know it.  There are many functional and useful features of EBM and these still form the cornerstone of EBM 2.0.  However, there are some components of EBM, specifically p-values and the concept of ‘statistical significance’ that have been corrupted and no longer provide us with a pathway towards scientific “truth“.

Together with my usual sparring partner – Justin Morgenstern – we spend a few hours discussing EBM 2.0 and how we might move ahead in the ‘post-p-values era‘.  This conversation has been edited down to two podcasts that cover all the concepts that Anand has written about.  The first episode is below.  The references are listed below as well – so you can stay skeptical and read the base literature for yourself.

If you want to hear a short version of Anand’s treatise then check out the video below.

Please go to the EBM 2.0 website for more data, details and debate.

REFERENCES:

• The problem with EBM
  • Ioannidis, J. P. A. (2005). “Why Most Published Research Findings Are False.” PLoS Medicine 2(8): e124.
  • Prasad, V., et al. (2013). “A Decade of Reversal: An Analysis of 146 Contradicted Medical Practices.” Mayo Clin Proc 88(8): 790-798.
  • Herrera-Perez, D., et al. (2019). “A comprehensive review of randomized clinical trials in three medical journals reveals 396 medical reversals.” eLife 8: e45183.
• Bias
  • Pannucci, C. J. and E. G. Wilkins (2010). “Identifying and avoiding bias in research.” Plast Reconstr Surg 126(2): 619-625.
  • Jones, C. W., et al. (2013). “Non-publication of large randomized clinical trials: cross sectional analysis.” Bmj 347: f6104.
  • Ioannidis, J. P. A. (2019). “What Have We (Not) Learnt from Millions of Scientific Papers with P Values?” The American Statistician 73(sup1): 20-25.
• Chance: p values, statistical significance and Bayesian Analysis
  • Nuzzo, R. (2014). “Scientific Method: Statistical Errors.” Nature 506(7487): 150-152.
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  • Greenland, S., et al. (2016). “Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.
  • “Wasserstein, R. L. and N. A. Lazar (2016). “The ASA’s Statement on p -Values: Context, Process, and Purpose.” The American Statistician 70(2): 129-133
  • Wasserstein, R. L., et al. (2019). “Moving to a World Beyond “ p  < 0.05”.” The American Statistician 73(1): 1-19.
  • Amrhein, V., et al. (2019). “Scientists rise up against statistical significance.” Nature 567(7748): 305
  • Benjamin, D. J. and J. O. Berger (2019). “Three Recommendations for Improving the Use of p-Values.” The American Statistician 73(sup1): 186-191.