We use Monte Carlo analysis (@Risk; Palisade, Version 4.5) to derive 95% uncertainty intervals for all outcome measures and to determine probabilities of intervention cost-effectiveness against a cost-effectiveness threshold of A$50,000 per DALY [6], [8], [32].
Figure 1b depicts that irrespective of the amount a decision maker is willing to pay, the probability of the intervention treatment being more cost effective than the control treatment is at most 60%.
Finding an association between increasing failure-to-intervention-time and probability of failed intervention would have important implications for service delivery, healthcare costs and patient outcomes.
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.
It provides a simple process for calculating the rank probabilities of interventions, incorporates prior knowledge, and fits more flexible models.
Our main effect model uses the following specification: (1) L o g i t (P ) = L n (P / 1 - P ) = β 0 + β i V i + β i V i + … + ε (1) P is the probability of an intervention to be selected as the most qualifying intervention based on the joint inclusion of all relevant criteria and their relative weights.
A low Bayes factor together with a low P-value will correspond to a high probability of an intervention effect similar to or greater than the anticipated intervention effect used in the calculation of the required information size.
