Bayesian probabilistic statements for communicating clinical trial results.
7 affiliations
- 1Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland. [email protected].
- 2Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland. [email protected].
- 3Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
- 4Department of Cardiology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
- 5Department of Neurology, Inselspital, University Hospital Bern and University of Bern, Bern, Switzerland.
- 6Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- 7Division of Clinical Epidemiology, University Hospitals of Geneva, Geneva, Switzerland.
Abstract
Background
Clinical trials are often analyzed using frequentist methods and results are interpreted according to their statistical significance. However, p-values and effect measures such as odds or hazard ratios are poorly understood by researchers and policymakers, and even less by patients and clinicians. Moreover, communicating uncertainty around treatment effects is challenging. Probabilistic statements from Bayesian analyses (e.g., the probability that a drug is more effective than placebo) offer an intuitive way to convey results and uncertainty, may provide insight, and support clinical decision-making. We aimed to (i) empirically evaluate the utility of probabilistic statements by re-analyzing data from previously published trials using Bayesian methods, and (ii) provide instructional examples of how probabilistic statements can be used to communicate and interpret results from clinical trials.Methods
We analyzed data on primary outcomes obtained from trials previously published in leading medical journals, using Bayesian methods. We included 169 primary outcomes from 130 negative trials (p-values > 0.05) across various medical fields, and 234 outcomes from 225 positive trials (p-value < 0.05) in oncology. We used uninformative priors for illustration, supplementing them with skeptical priors in sensitivity analyses. We estimated the probabilities that active treatments were more effective or safe than controls, for each outcome. We also estimated probabilities that the treatment effects were above specific thresholds for clinical significance. We described three of the included trials in more detail and additionally analyzed one trial in neurology as didactical case studies, to illustrate the use of probabilistic statements.Results
So-called negative studies may sometimes yield clinically relevant evidence of an effect: in 26/169 (15%) cases we found > 90% probability that the experimental treatment is better than control; for 12/169 (7%) of them, this probability was > 95%. Conversely, positive trials may not always show evidence of clinically meaningful effects: in 25/234 (11%) of the positive oncology trials, there was less than 50% probability that treatment reduces hazards by 20% or more. Our case studies illustrate how to use probabilistic statements in medical decision making in practice.Conclusions
Relying on statistical significance without considering clinical importance can mislead decision-making and potentially harm patients. Probabilistic statements from Bayesian analyses can complement traditional approaches to communicating clinical trial results.
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BibTeX
@article{efthimiou2026bayesian,
title = {{Bayesian probabilistic statements for communicating clinical trial results.}},
author = {Orestis Efthimiou and Konstantina Chalkou and George Cm Siontis and Urs Fischer and Thomas Perneger and Georgia Salanti and Angèle Gayet-Ageron},
journal = {BMC medicine},
year = {2026},
month = oct,
doi = {10.1186/s12916-026-05208-w},
url = {https://europepmc.org/article/MED/42850643},
note = {CC BY},
}
RIS
TY - JOUR
TI - Bayesian probabilistic statements for communicating clinical trial results.
AU - Orestis Efthimiou
AU - Konstantina Chalkou
AU - George Cm Siontis
AU - Urs Fischer
AU - Thomas Perneger
AU - Georgia Salanti
AU - Angèle Gayet-Ageron
PY - 2026
DA - 2026/10/08
JO - BMC medicine
DO - 10.1186/s12916-026-05208-w
UR - https://europepmc.org/article/MED/42850643
AB - Background Clinical trials are often analyzed using frequentist methods and results are interpreted according to their statistical significance. However, p-values and effect measures such as odds or hazard ratios are poorly understood by researchers and policymakers, and even less by patients and clinicians. Moreover, communicating uncertainty around treatment effects is challenging. Probabilistic statements from Bayesian analyses (e.g., the probability that a drug is more effective than placebo) offer an intuitive way to convey results and uncertainty, may provide insight, and support clinical decision-making. We aimed to (i) empirically evaluate the utility of probabilistic statements by re-analyzing data from previously published trials using Bayesian methods, and (ii) provide instructional examples of how probabilistic statements can be used to communicate and interpret results from clinical trials.Methods We analyzed data on primary outcomes obtained from trials previously published in leading medical journals, using Bayesian methods. We included 169 primary outcomes from 130 negative trials (p-values > 0.05) across various medical fields, and 234 outcomes from 225 positive trials (p-value < 0.05) in oncology. We used uninformative priors for illustration, supplementing them with skeptical priors in sensitivity analyses. We estimated the probabilities that active treatments were more effective or safe than controls, for each outcome. We also estimated probabilities that the treatment effects were above specific thresholds for clinical significance. We described three of the included trials in more detail and additionally analyzed one trial in neurology as didactical case studies, to illustrate the use of probabilistic statements.Results So-called negative studies may sometimes yield clinically relevant evidence of an effect: in 26/169 (15%) cases we found > 90% probability that the experimental treatment is better than control; for 12/169 (7%) of them, this probability was > 95%. Conversely, positive trials may not always show evidence of clinically meaningful effects: in 25/234 (11%) of the positive oncology trials, there was less than 50% probability that treatment reduces hazards by 20% or more. Our case studies illustrate how to use probabilistic statements in medical decision making in practice.Conclusions Relying on statistical significance without considering clinical importance can mislead decision-making and potentially harm patients. Probabilistic statements from Bayesian analyses can complement traditional approaches to communicating clinical trial results.
SN - 1741-7015
ER -