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Background Fatigue has a major influence on the quality of life of people with multiple sclerosis. The Fatigue Severity Scale is a frequently used patient-reported measure of fatigue impact, but does not generate the health state utility values required to inform cost-effectiveness analysis, limiting its applicability within decision-making contexts. The objective of this study was to use statistical mapping methods to convert Fatigue Severity Scale scores to health state utility values from three preference-based measures: the EQ-5D-3L, SF-6D and Multiple Sclerosis Impact Scale-8D. Methods The relationships between the measures were estimated through regression analysis using cohort data from 1056 people with multiple sclerosis in South West England. Estimation errors were assessed and predictive performance of the best models as tested in a separate sample ( n = 352). Results For the EQ-5D and the Multiple Sclerosis Impact Scale-8D, the best performing models used a censored least absolute deviation specification, with Fatigue Severity Scale total score, age and gender as predictors. For the SF-6D, the best performing model used an ordinary least squares specification, with Fatigue Severity Scale total score as the only predictor. Conclusions Here we present algorithms to convert Fatigue Severity Scales scores to health state utility values based on three preference-based measures. These values may be used to estimate quality-adjusted life-years for use in cost-effectiveness analyses and to consider the health-related quality of life of people with multiple sclerosis, thereby informing health policy decisions. Electronic supplementary material The online version of this article (10.1186/s12955-019-1205-y) contains supplementary material, which is available to authorized users.

OBJECTIVES: Health technology assessment methods traditionally reserved for the appraisal of pharmaceuticals can also be used to demonstrate the value for money of products classed as 'borderline substances', such as thickeners, to assist patients with dysphagia. Different thickeners have different characteristics and the preference for these characteristics is unclear. The aim of the study is to quantify the value placed on different characteristics of thickeners by Speech and Language Therapists (SaLTs) and other Health Care Professionals (HCPs) using a discrete choice experiment (DCE). Value is expressed in monetary (£) form. METHODS: A series of interviews were conducted to identify the attributes of thickening agents that HCPs consider important (n¼15). The attributes and their associated levels were tested and validated in interviews with SaLTs (n¼3). The key attributes identified from the interviews were: taste, texture, ease of mixing, stability, cost, and training. Each attribute was classified as two or three categories, except cost which was a continuous variable. D-optimal statistical design was employed and 24 paired choice sets across two blocks were identified. In each choice question, the participants were presented with two descriptions of different thickening agents and asked to make a choice based on which they preferred. The DCE online survey was developed and administered to HCPs (n¼102). RESULTS: HCPs placed a significant value on the taste, texture, stability, and cost of a thickener, and these variables were statistically significant. The willingness to pay for neutral taste compared to unpleasant taste or aftertaste was highest at £156, followed by smooth consistency at £59. SaLTs (n¼61) valued neutral taste and smooth consistency much more than the non-SaLTs who participated in the study. CONCLUSIONS: The study found that HCPs consider neutral taste, smooth consistency, rapid thickening, and not thickening when left over time as important attributes in a thickener.

Objectives: Probabilistic sensitivity analysis (PSA) addresses parameter uncertainty inherent in a decision problem, and provides the most accurate incremental cost-effectiveness ratio (ICER) for non-linear models. Literature on the number of simulations required suggests a "sufficient number", or until "convergence" which is seldom defined. Some methods to assess convergence of the mean ICER (such as "jackknifing") exist, but are rarely used. In this study, we aim to define convergence and use empirical data to propose simulation numbers for different outcomes. MethOds: 250,000 individual simulations were drawn from a variety of cohort models (n= 30) constructed in different disease areas. Random samples were drawn to simulate 1,000 PSAs using up to 25,000 simulations per PSA. We identified the numbers of simulations required for the mean values of the PSAs to be reasonably close to the 'true' results, defined as the mean results of the 250,000 individual simulations. Convergence to the edges of the distribution was also analysed. The results considered were the costs, quality-adjusted life years (QALYs), incremental net monetary benefit (INMB) and ICER. Results: Costs, QALYs and INMB were within 1% of the mean by 1,000 simulations in over 98% of scenarios. For the ICER at 1,000 simulations the ICER was within ±£500 of the ICER in 94.7% of scenarios, which increased to 98.6% at 5,000 simulations, and to 99.3% at 10,000 simulations. The edges of the distributions took longer to stabilise in all cases, not stabilising within 1% until over 10,000 simulations for nearly 90% of scenarios. cOnclusiOns: Health economic models have sufficient complexity that low numbers of samples cannot guarantee accurate results. We recommend 10,000 simulations be used for decision making, which gives suitably stable mean results without excessive computational burden, unless convergence before this point has been demonstrated either via repeated sampling, or jackknifing. PRM89 aPPRoacHes to Modelling tHe cost-effectiveness of inteRventions foR HeaRt failuRe: a systeMatic Review