MOESM1 of Estimating the cost-effectiveness of daclatasvir + sofosbuvir versus sofosbuvir + ribavirin for patients with genotype 3 hepatitis C virus

Additional file 1: Figure S1. Schematic of the cohort-based Markov simulation model

Changes in the Morphology and Proliferation of Astrocytes Induced by Two Modalities of Chemically Functionalized Single-Walled Carbon Nanotubes are Differentially Mediated by Glial Fibrillary Acidic Protein

Alterations in glial fibrillary acidic protein (GFAP) levels accompany the changes in the morphology and proliferation of astrocytes induced by colloidal solutes and films of carbon nanotubes (CNTs). To determine if GFAP is required for the effects of CNTs on astrocytes, we used astrocytes isolated from GFAP null mice. We find that selected astrocytic changes induced by CNTs are mediated by GFAP, i.e., perimeter, shape, and cell death for solutes, and proliferation for films

Estimated per patient costs (£000s) related specifically to HCV related complications (excluding any HCV therapy costs) stratified by age, discounting, fibrosis stage and SVR.

<p>Estimated per patient costs (£000s) related specifically to HCV related complications (excluding any HCV therapy costs) stratified by age, discounting, fibrosis stage and SVR.</p

Default disease state transition rates applied in the model.

<p>*Transition rates are influenced by the coefficients presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.t002" target="_blank">Table 2</a>; rates presented here are the mean values as reported in the original study.</p><p>Default disease state transition rates applied in the model.</p

Estimated per-patient life expectancy stratified by age, discounting, current fibrosis stage and SVR.

<p>Estimated per-patient life expectancy stratified by age, discounting, current fibrosis stage and SVR.</p

Modelling assumptions utilised when populating the MONARCH model with study-specific input data.

<p>Modelling assumptions utilised when populating the MONARCH model with study-specific input data.</p

Estimated per-patient QALYs stratified by age, discounting, current fibrosis stage and SVR.

<p>Estimated per-patient QALYs stratified by age, discounting, current fibrosis stage and SVR.</p

Predicted rates of liver-related mortality (validation to [26, 27]) (left) and all-cause mortality (UK life tables) (right) as estimated by the MONARCH model.

<p>Predicted rates of liver-related mortality (validation to [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref026" target="_blank">26</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref027" target="_blank">27</a>]) (left) and all-cause mortality (UK life tables) (right) as estimated by the MONARCH model.</p

Predicted ICER values utilizing the MONARCH cost-effectiveness model compared to original study-specific ICER values as reported in 8 UK cost-effectiveness studies [8,9,38,39,41–44].

<p>Predicted ICER values utilizing the MONARCH cost-effectiveness model compared to original study-specific ICER values as reported in 8 UK cost-effectiveness studies [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref008" target="_blank">8</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref009" target="_blank">9</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref038" target="_blank">38</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref039" target="_blank">39</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref041" target="_blank">41</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117334#pone.0117334.ref044" target="_blank">44</a>].</p

Flow diagram of the MONARCH model.

<p>Flow diagram of the MONARCH model.</p