Citation Information :
Ho KM, Lee A. Using Bayesian Hypothesis-testing to Reanalyze Randomized Controlled Trials: Does it Always Tell the Truth, the Whole Truth and Nothing but the Truth?. Indian J Crit Care Med 2024; 28 (11):1005-1008.
Adequately powered randomized controlled trials (RCTs) are considered the highest level of evidence in guiding clinical practice. Reports using Bayesian hypothesis-testing to reanalyze RCTs are increasing. One distinct advantage of Bayesian analysis is that we can obtain a range of numerical probabilities that reflect how likely a study intervention is more effective than the alternative after considering both pre-existing available evidence and the alternate hypotheses. A recent analysis of critical care trials showed that some trials with an indeterminate result according to the frequentist analysis could have a high probability of being effective when reinterpreted by Bayesian analysis. In this perspective article, we will discuss the caveats in interpreting the results of Bayesian reanalysis of RCTs before we change clinical practice. When overoptimistic hypothesis prior probabilities are used, it carries a risk to translate noises into false signals. Using Bayes factors (BFs) to quantify evidence contained in data (by the ratio of the probability of data under each hypothesis) is thus more preferable than using a single prior probability, such that the BF approach becomes the mainstream in Bayesian hypothesis-testing. Still, BFs are dependent on the prior parameter distributions; comparing different hypotheses would invariably result in different results.
Pocock SJ, Ware JH. Translating statistical findings into plain English. Lancet 2009;373(9679):1926–1928. DOI: 10.1016/S0140-6736(09)60499-2.
Harhay MO, Blette BS, Granholm A, Moler FW, Zampieri FG, Goligher EC, et al. A Bayesian interpretation of a pediatric cardiac arrest trial (THAPCA-OH). NEJM Evid 2023;2(1):EVIDoa2200196. DOI: 10.1056/EVIDoa2200196.
Zampieri FG, Damiani LP, Bakker J, Ospina-Tascón GA, Castro R, Cavalcanti AB, et al. Effects of a resuscitation strategy targeting peripheral perfusion status versus serum lactate levels among patients with septic shock. A Bayesian reanalysis of the ANDROMEDA-SHOCK trial. Am J Respir Crit Care Med 2020;201(4):423–429. DOI: 10.1164/rccm.201905-0968OC.
Goligher EC, Tomlinson G, Hajage D, Wijeysundera DN, Fan E, Jüni P, et al. Extracorporeal membrane oxygenation for severe acute respiratory distress syndrome and posterior probability of mortality benefit in a post hoc Bayesian analysis of a randomized clinical trial. JAMA 2018;320(21):2251–2259. DOI: 10.1001/jama.2018.14276.
Lammers D, Richman J, Holcomb JB, Jansen JO. Use of Bayesian statistics to reanalyze data from the pragmatic randomized optimal platelet and plasma ratios trial. JAMA Netw Open 2023;6(2):e230421. DOI: 10.1001/jamanetworkopen.2023.0421.
Ma WJ, Kording KP, Goldreich D. Bayesian Models of Perception and Action: An Introduction. MIT press. 2023; chapter 2 Pages: 43–56. Available from: https://mitpress.mit.edu/9780262047593/bayesian-models-of-perception-and-action/. ISBN: 9780262047593.
Bours MJ. Bayes’ rule in diagnosis. J Clin Epidemiol 2021;131:158–160. DOI: 10.1016/j.jclinepi.2020.12.021.
Sidebotham D, Barlow CJ, Martin J, Jones PM. Interpreting frequentist hypothesis tests: Insights from Bayesian inference. Can J Anaesth 2023;70(10):1560–1575. DOI: 10.1007/s12630-023-02557-5.
Yarnell CJ, Abrams D, Baldwin MR, Brodie D, Fan E, Ferguson ND, et al. Clinical trials in critical care: Can a Bayesian approach enhance clinical and scientific decision making? Lancet Respir Med 2021;9(2):207–216. DOI: 10.1016/S2213-2600(20)30471-9.
Gibbs NM, Weightman WM. Beta errors in anaesthesia randomized controlled trials in which no statistical significance is found: Is there an elephant in the room? Anaesth Intensive Care 2022;50(3):153–158. DOI: 10.1177/0310057X221086590.
Ranganathan P, Cs P. An introduction to statistics: Understanding hypothesis testing and statistical errors. Indian J Crit Care Med 2019;23(Suppl 3):S230–S231. DOI: 10.5005/jp-journals-10071-23259.
Edwards W, Lindman H, Savage LJ. Bayesian Statistical Inference for Psychological Research. 1963;70(3):531-578; In Samuel Kotz & Norman L. Johnson (Eds.), Breakthroughs in Statistics, Volume 1 Foundations and Basic Theory. Springer Series in Statistics. Available from: https://psycnet.apa.org./record/1964-00040-001.
Ferguson J. Bayesian interpretation of p values in clinical trials. BMJ Evid Based Med 2022;27(5):313–316. DOI: 10.1136/bmjebm-2020-111603.
Perneger TV. How to use likelihood ratios to interpret evidence from randomized trials. J Clin Epidemiol 2021;136:235–242. DOI: 10.1016/j.jclinepi.2021.04.010.
Johnson VE, Pramanik S, Shudde R. Bayes factor functions for reporting outcomes of hypothesis tests. Proc Natl Acad Sci U S A 2023;120(8):e2217331120. DOI: 10.1073/pnas.2217331120.
Kruschke JK, Liddell TM. Bayesian data analysis for newcomers. Psychon Bull Rev 2018;25(1):155–177. DOI: 10.3758/s13423-017- 1272-1.
Wei Z, Yang A, Rocha L, Miranda MF, Nathoo FS. A review of Bayesian hypothesis testing and its practical implementations. Entropy (Basel) 2022;24(2):161. DOI: 10.3390/e24020161.
Udani S. A good workman never blames his tools: Appropriate use of severity of illness scoring systems determines their utility! Indian J Crit Care Med 2020;24(8):628–629. DOI: 10.5005/jp-journals-10071- 23545.