Bayesian Statistical Inference

How it works

Benefits

Dosage & preparation

Comparisons

Background

Highlighted claims

• The study switched to Bayesian statistical inference after actual enrolment averaged only 20 patients per month, revising the expected total from 1,368 to approximately 450 patients. — Evidence-based team intervention to reduce diagnostic errors in anaemia and CKD diagnoses in primary care: protocol for a stepped-wedge cluster RCT
• Bayesian analysis combines the prior distribution of plausible parameter values with the observed data likelihood to form the posterior distribution. — Evidence-based team intervention to reduce diagnostic errors in anaemia and CKD diagnoses in primary care: protocol for a stepped-wedge cluster RCT
• Intervention adoption decisions are based on the posterior distribution of effect sizes rather than p-value thresholds. — Evidence-based team intervention to reduce diagnostic errors in anaemia and CKD diagnoses in primary care: protocol for a stepped-wedge cluster RCT
• The decision threshold for recommending intervention adoption is a posterior probability greater than 0.75 that the treatment odds ratio exceeds 1.2. — Evidence-based team intervention to reduce diagnostic errors in anaemia and CKD diagnoses in primary care: protocol for a stepped-wedge cluster RCT
• Bayesian inference offers improved efficiency with smaller samples and enables clinically intuitive probability statements about intervention benefit that frequentist methods cannot provide. — Evidence-based team intervention to reduce diagnostic errors in anaemia and CKD diagnoses in primary care: protocol for a stepped-wedge cluster RCT
• At n=450, Monte Carlo simulations project 83% Bayesian power at ICC=0.05, outperforming equivalent frequentist power of 77%. — Evidence-based team intervention to reduce diagnostic errors in anaemia and CKD diagnoses in primary care: protocol for a stepped-wedge cluster RCT