John Park: Poisoned Priors: Will You Drink from This Well?(Guest Post)


John Park, MD
Radiation Oncologist
Kansas City VA Medical Center

Poisoned Priors: Will You Drink from This Well?

As an oncologist, specializing in the field of radiation oncology, “The Statistics Wars and Intellectual Conflicts of Interest”, as Prof. Mayo’s recent editorial is titled, is one of practical importance to me and my patients (Mayo, 2021). Some are flirting with Bayesian statistics to move on from statistical significance testing and the use of P-values. In fact, what many consider the world’s preeminent cancer center, MD Anderson, has a strong Bayesian group that completed 2 early phase Bayesian studies in radiation oncology that have been published in the most prestigious cancer journal —The Journal of Clinical Oncology (Liao et al., 2018 and Lin et al, 2020). This brings about the hotly contested issue of subjective priors and much ado has been written about the ability to overcome this problem. Specifically in medicine, one thinks about Spiegelhalter’s classic 1994 paper mentioning reference, clinical, skeptical, or enthusiastic priors who also uses an example from radiation oncology (Spiegelhalter et al., 1994) to make his case. This is nice and all in theory, but what if there is ample evidence that the subject matter experts have major conflicts of interests (COIs) and biases so that their priors cannot be trusted?  A debate raging in oncology, is whether non-invasive radiation therapy is as good as invasive surgery for early stage lung cancer patients. This is a not a trivial question as postoperative morbidity from surgery can range from 19-50% and 90-day mortality anywhere from 0–5% (Chang et al., 2021). Radiation therapy is highly attractive as there are numerous reports hinting at equal efficacy with far less morbidity. Unfortunately, 4 major clinical trials were unable to accrue patients for this important question. Why could they not enroll patients you ask? Long story short, if a patient is referred to radiation oncology and treated with radiation, the surgeon loses out on the revenue, and vice versa. Dr. David Jones, a surgeon at Memorial Sloan Kettering, notes there was no “equipoise among enrolling investigators and medical specialties… Although the reasons are multiple… I believe the primary reason is financial” (Jones, 2015). I am not skirting responsibility for my field’s biases. Dr. Hanbo Chen, a radiation oncologist, notes in his meta-analysis of multiple publications looking at surgery vs radiation that overall survival was associated with the specialty of the first author who published the article (Chen et al, 2018). Perhaps the pen is mightier than the scalpel!

Currently, there is one surgery vs radiation trial that is accruing well, the VALOR study, a Veterans Affairs (VA) only trial. Although only 9 VA medical centers were involved in 2020, it had enrolled more participants than all previous major (phase 3) trials combined (Moghanaki and Hagan, 2020). I do not believe it is too bold to say that a major portion of this success is due to the fact there are no financial incentives for the surgeons or radiation therapists at the VA (i.e. VA physicians are salaried and do not receive payment per patient).

Here are some clear examples of what I call “poisoned priors” due to COIs. Whether financial or for prestige (would you want to be known as the inferior treatment modality for one of the most common cancers?), the COIs loom large. Many of the specialists in question are highly biased, with exposed COIs. Are we to trust priors constructed from them? Will the errors really be contained within the posteriors from these biased priors? In order to overcome this, you say that you want to use an uninformative or weakly informative prior as a statistical method to judge incoming data? Then what’s the point of having prior knowledge, in this case the surgeons’ and radiation oncologists’ priors who are the subject matter experts, if you are not willing to use them? Indeed as Prof. Mayo notes “It may be retorted that implausible inferences will indirectly be blocked by appropriate prior degrees of belief (informative priors), but this misses the crucial point. The key function of statistical tests is to constrain the human tendency to selectively favor views they believe” (Mayo, 2021). If this statement holds for appropriate prior degrees of belief, how much more is it relevant when we can show that those involved have inappropriate prior degrees belief?

These types of poisoned priors are ubiquitous in medicine and must be taken into account — we haven’t even dealt with “Big Pharma” (and don’t get me started)! We must not give up the apparatus of the phase 3 randomized trial, with its randomization, blinding, multiplicity control, and preregistered statistical thresholds for type I and II error control, which is the best form of severe testing we have for our patients.


  • Chang JY, Mehran RJ, Feng L, et al. Stereotactic ablative radiotherapy for operable stage I non-small-cell lung cancer (revised STARS): long-term results of a single-arm, prospective trial with prespecified comparison to surgery. The Lancet Oncology. 2021;22(10):1448-1457. doi:10.1016/S1470-2045(21)00401-0
  • Chen H, Laba JM, Boldt RG, et al. Stereotactic Ablative Radiation Therapy Versus Surgery in Early Lung Cancer: A Meta-analysis of Propensity Score Studies. Int J Radiat Oncol Biol Phys. 2018;101(1):186-194. doi:10.1016/j.ijrobp.2018.01.064
  • Jones DR. Do we know bad science when we see it? The Journal of Thoracic and Cardiovascular Surgery. 2015;150(3):472-473. doi:10.1016/j.jtcvs.2015.07.032
  • Liao Z, Lee JJ, Komaki R, et al. Bayesian Adaptive Randomization Trial of Passive Scattering Proton Therapy and Intensity-Modulated Photon Radiotherapy for Locally Advanced Non-Small-Cell Lung Cancer. J Clin Oncol. 2018;36(18):1813-1822. doi:10.1200/JCO.2017.74.0720
  • Lin SH, Hobbs BP, Verma V, et al. Randomized Phase IIB Trial of Proton Beam Therapy Versus Intensity-Modulated Radiation Therapy for Locally Advanced Esophageal Cancer. J Clin Oncol. 2020;38(14):1569-1579. doi:10.1200/JCO.19.02503
  • Mayo DG. The statistics wars and intellectual conflicts of interest. Conserv Biol. Published online December 6, 2021. doi:10.1111/cobi.13861
  • Moghanaki D, Hagan M. Strategic Initiatives for Veterans with Lung Cancer. Fed Pract. 2020;37(Suppl 4):S76-S80. doi:10.12788/fp.0019
  • Razi, S. S., Kodia, K., Alnajar, A., Block, M. I., Tarrazzi, F., Nguyen, D., & Villamizar, N. (2020). Lobectomy Versus Stereotactic Body Radiotherapy In Healthy Octogenarians With Stage I Lung Cancer. The Annals of Thoracic Surgery, S000349752031448X.
  • Spiegelhalter DJ, Freedman LS, Parmar MKB. Bayesian Approaches to Randomized Trials. Journal of the Royal Statistical Society Series A (Statistics in Society). 1994;157(3):357-416. doi:10.2307/2983527

All commentaries on Mayo (2021) editorial until Jan 31, 2022 (more to come*)

Ionides and Ritov

*Let me know if you wish to write one




Categories: ASA Task Force on Significance and Replicability, Bayesian priors, PhilStat/Med, statistical significance tests | Tags:

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4 thoughts on “John Park: Poisoned Priors: Will You Drink from This Well?(Guest Post)

  1. Insightful post, clear and persuasive.

  2. John:
    Thank you so much for your commentary. I learned a lot from that session we were in at the ASTRO conference recently on radiation oncology. Do other doctors speak up about this? As you know, I included one of the examples in my editorial. (It was discussed first in this blogpost
    I’m very grateful for the very useful references here as well. I look forward to future discussions and to consulting you on cases I come across!

  3. John Park

    Prof. Mayo,

    Only a small circle of doctors talk about these issues, but I believe that circle is widening. With social media and all the talks of scientific research in the media, this has become a hotter topic.

    It was also great working with you on our ASTRO presentation and look forward to future collaborations!

  4. Pingback: Paul Daniell & Yu-li Ko commentaries on Mayo’s ConBio Editorial | Error Statistics Philosophy

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