Aim To evaluate the cost-effectiveness of therapeutic strategies initiated at different stages of liver fibrosis using three direct-acting antivirals (DAAs), sofosbuvir-ledipasvir (SL), glecaprevir-pibrentasvir (GP), and elbasvir plus grazoprevir (E/G), for Japanese patients with chronic hepatitis C (CHC) genotype 1. Methods We created an analytical decision model reflecting the progression of liver fibrosis stages to evaluate the cost-effectiveness of alternative therapeutic strategies applied at different fibrosis stages. We compared six treatment strategies: treating all patients regardless of fibrosis stage (TA), treating individual patients with one of four treatments starting at four respective stages of liver fibrosis progression (F1S: withholding treatment at stage F0 and starting treatment from stage F1 or higher, and three successive options, F2S, F3S, and F4S), and administering no antiviral treatment (NoRx). We adopted a lifetime horizon and Japanese health insurance payers’ perspective. Results The base case analysis showed that the incremental quality-adjusted life years (QALY) gain of TA by SL, GP, and E/G compared with the strategies of starting treatments for patients with the advanced fibrosis stage, F2S, varied from 0.32 to 0.33, and the incremental cost-effectiveness ratios (ICERs) were US$24,320, US$18,160 and US$17,410 per QALY, respectively. On the cost-effectiveness acceptability curve, TA was most likely to be cost-effective, with the three DAAs at the willingness to pay thresholds of US$50,000. Conclusions Our results suggested that administration of DAA treatment for all Japanese patients with genotype 1 CHC regardless of their liver fibrosis stage would be cost-effective under ordinary conditions.

Objectives: Complication costs can typically be modelled by Uniform and Pert distributions but it remains important to obtain an accurate estimation of variance since unknown variance is more problematic than unstable variance. The objective was (i) to compare the central tendency of the Uniform and Pert distributions for their respective ability to decrease complication cost variance and (ii) illustrate use of the CLT to ensure that the complication cost for each patient is a random sample from the assumed distribution for 100 theoretical patients. The CLT is used since it is incorrect to simply multiply a random number from the cost distribution by the number of patients. MethOds: A random set of costs (Normal distribution; n = 500; mean = 100; SD = 25) was generated (MS EXCEL). A Uniform distribution was fitted using the data range and a Pert distribution was fitted using the minimum, most likely (mean of the generated data), and maximum values. The mean and SD of both distributions were simulated (@Risk; Palisade) and compared. The "most likely" value of the Pert distribution was varied to include [mean +SD] and [mean -SD] to evaluate the effect of imprecise "most likely" estimates on the simulated variance. Monte-Carlo simulation was used with 10000 iterations. Results: Mean of the Uniform distribution was 99 (SD = 44), while the Pert distribution returned SD's of ~28 for all "most likely" scenarios. Mean simulated total cost for 100 patients using the CLT was 9969 (SD = 288.33; minimum = 8788; maximum = 11100; 95% CI = 9400 -10500). cOnclusiOns: The Pert distribution is robust to varied "most-likely" value estimates and decreases estimated variance over the Uniform distribution. Application of the CLT ensures that each patient's complication cost is independent of the next and that the spread of costs is not exaggerated.Objectives: This paper proposes a data-driven method to strengthen predictions from discrete choice experiments (DCEs). DCEs are used as predictive tools when there is little or no observational data available to predict uptake of a service or good, however, there is little evidence assessing how well DCEs predict behaviour. This review is the first to quantitatively assess the external validity of published DCE studies, before setting out a modelling framework for DCE estimates to be adjusted such that they better reflect real-world choices. An application to predicting demand for new HIV prevention products is presented. MethOds: We conduct a systematic review and meta-analysis to summarise how well DCE predictions reflect real-world choices. We synthesise results using bivariate mixed-effects logistic regression to produce pooled sensitivity and specificity estimates. A probabilistic approach is proposed to incorporate information to model uptake, and applied to a dataset predicting demand for new HIV prevention products. Results: Five studies were included in the meta-analysis. In total we consider 716 observations, where choices were correctly predicted 80% of the time; 53% of incorrect...

These costs yielded an ICER of-4170 for the SOF+LDV+RBV,-9515 for the SOF+DCV and-2289 for the SOF+RBV. SOF+DCV regimen was the most cost saving option for cirrhotic and non-cirrhotic patients than the other treatment regimens. Deterministic sensitivity analyses remain robust. ConClusions: This study concludes that SOF+DCVregimen is the most cost saving option that yields the most favorable future health economic outcomes compared to the SOF+pegIFN+RBV group across a broad spectrum of patients, including those with cirrhosis.