ReviewDeutsches Arzteblatt international2025
Introduction to Bayesian Statistics: Part 36 of a Series on the Evaluation of Scientific Publications.
Review in Deutsches Arzteblatt international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- An Epidemic of Complexity: Polysubstance Use Fatal Overdose Typologies Across Cook County, Illinois, Neighborhoods, 2017‒2022.American journal of public health · 2026Article
- In Reply.Deutsches Arzteblatt international · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe analysis of a study with Bayesian statistics makes use of additional information to supplement the new study data. In this review, we explain the principles of the application of this method in clinical research.
methodsThe concept of Bayesian statistics is introduced and explained with the aid of an illustrative example from a drug approval study. Its major aspects are discussed. The existing prior knowledge is formulated as a probability distribution of an odds ratio. Multiple scenarios are shown to demonstrate how a suitable prior distribution is determined and how it can affect the final result.
resultsBayesian statistics makes use of prior knowledge, e.g., the findings of earlier clinical trials, and combines the prior probability distribution with the findings of the current study for statistical analysis. The suitability and applicability of the prior knowledge in question must be assessed and the prior knowledge weighted accordingly, and any uncertainties must be taken into account in the analysis. The result that is derived is called the posterior distribution of the parameters of interest and is summarized in terms of point estimators and credibility intervals. In contrast to classical statistics, results of this type permit direct quantitative statements on the probability of parameter values and on the probabilities of the null and alternative hypotheses (in one-sided statistical tests).
conclusionCombining the current study findings with prior knowledge can enable the more precise estimation of a treatment effect, or else lessen the number of subjects needed for a clinical trial. Central elements of Bayesian statistics are the selection and weighting of prior knowledge; subjective judgements must be made. Bayesian techniques require a precise description of the methods applied, meticulous study of the available literature, and experience in the mathematical representation of the results.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.