Our results provide credible transmission probabilities for approximately 25 different serotypes.
This is the first study to provide credible transmission probabilities for a wide range of serotypes.
The statistical results will be presented in OR (with credible interval) and probability ranking.
*Significance at p <0.01. Figure 5 shows the estimated total spatial effects (left) and the corresponding 80% (credible interval) posterior probability map (right) of cholera risk.
As the present study is a Bayesian analysis we refer to credible intervals (the probability that the true value is contained within the interval) rather than confidence intervals; instead of statistically significant differences, we refer to important differences (95%% credible interval for hazard ratio does not cross 1.0).
Network meta-analysis was performed within a Bayesian framework computing odds ratios (95% credible intervals) and probability of being best of any given treatment (Pbest) with a fixed-effect hierarchical model, sampling posterior probabilities by means of Markov chain Monte Carlo (MCMC) methods with Gibbs sampling from 150,000 iterations obtained after a 50,000-iteration training phase [ 22, 23].
CrI, credible interval (Bayesian probability interval); HAQ, health assessment questionnaire; NMA, network meta-analysis; OR, odds ratio; PsARC, Psoriatic Arthritis Response Criteria; PASI, Psoriasis Area and Severity Index; WMD, weighted mean differences; NA, not applicable †significant result based on 95% CrI.
This cumulative distribution function makes it possible to calculate credible intervals and the probability that the effect studied is lower or higher than a particular value, given the data and prior probabilities.
†Coverage probability and credible interval (CI) mean width found with the quantile and the Highest Posterior Density (HPD) region method.
Validation of the model performance was done on the test locations (the remaining 15% of the data) The predictive ability of the model was assessed by specifically calculate different credible intervals with different probability coverage of the posterior predictive distribution and compare the percentage of test locations correctly predicted within these credible intervals (52).
