PMC:4636534 / 20465-21478 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4636534","sourcedb":"PMC","sourceid":"4636534","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4636534","text":"Alternatively, parameter uncertainty can be addressed by means of a probabilistic sensitivity analysis. Each uncertain model input parameter is assigned a distribution, based on the mean and standard deviation/error as found in the literature. Examples of distributions surrounding the input parameters can be found in Table 1. In the probabilistic sensitivity analysis, different samples are taken from these distributions. For each sample, the hypothetical cohort runs through the model based on these sampled probabilities, and costs and outcome are derived. This results in a range of estimates of costs and outcome, representing the uncertainty in the result of the cost-effectiveness estimation. This range in outcome is usually expressed as 95 % Confidence Intervals (CI), based on the probabilistic analysis. In our example, the expected costs per patient are € 15,266 (95 % CI € 14,072–16,440) and expected QALYs 1.30 (95 % CI 1.00–1.61), if all patients were to receive PET-CT at three months follow-up.","tracks":[]}