It provides a simple process for calculating the rank probabilities of interventions, incorporates prior knowledge, and fits more flexible models.
A bivariate analysis was performed to obtain the probability of intervention.
The probability of intervention being cost effective at cost effectiveness threshold of £30,000 per QALY was 47.9%.
The probability of intervention being more cost effective at £20,000 was 85% when considering total healthcare costs and 81% for combined healthcare and social care costs.
Among 91 network meta-analyses with a Bayesian approach, 52 reports gave the rank order of interventions; 40 of these gave the probabilities of each intervention being the best, 7 used absolute or cumulative rankograms, and 5 stated which intervention was the best.
We assessed whether the authors presented the probabilities of each intervention being the best, the probabilities of each intervention taking each possible rank (in an absolute or cumulative ranking curve), or the mean ranks (or the surface under the cumulative ranking curve) and whether authors stated which intervention was the best.
Adjusted probabilities of each intervention are calculated for each income quintile, controlling for age and comorbidities.
In particular, Cure et al 23 reported that the probability that the odds ratio between telaprevir and boceprevir was larger than 1, in contrast to conventional MTC meta-analyses that report probabilities of each intervention yielding larger effect estimates (eg, odds ratio) than all other considered treatments.
Aims to completely reduce the landslide risk at the site (negligible hazard probability) Environmental Effect of intervention Value Effect of intervention Value Water Time of inundation of the riverine zone Aims to completely reduce the inundations, but not designed for specific protection level.
Effects are estimated with Monte Carlo simulation using the combined full probability distributions of intervention effects on stillbirths, neonatal deaths and maternal deaths.
