VP100 Disease Modelling Approaches In Multiple Sclerosis

2017 ◽  
Vol 33 (S1) ◽  
pp. 194-195
Author(s):  
Paolo Cortesi ◽  
Nilhan Uzman ◽  
Matteo Ferrario ◽  
Lorenzo Giovanni Mantovani

INTRODUCTION:In the past decades the cost-effectiveness of new effective disease-modifying therapies (DMTs) for Relapsing Remitting Multiple Sclerosis (RRMS) form was assessed through decision analytical models. Recently, new treatment option for the Primary Progressive (PPMS) form was developed. Aim of this work was assessing the similarities and differences of PPMS and RRMS and their impact in the development of decision analytical model for PPMS.METHODS:Literature review was performed to retrieve information on natural history of PPMS and RRMS and impact of DMTs agents on the progression of these conditions. Further, a review of the published cost-effectiveness models for RRMS was performed. Based on these data, an analysis on the difference and similarities between the two MS forms that could have an impact on the development of decision analytical model for PPMS was performed.RESULTS:Based on the analysis, similar structure model used for RRMS could be applied for PPMS. Health states of the model could be based on Expanded Disability Status Scale score as already done for RRMS. The relapse events considered for RRMS should not be included in PPMS model, and no possibility to develop another form, as the Secondary Progressive, should be included. While RRMS models should include at least a second line treatment option due to alternative DMTs available, only first treatment line should be considered for PPMS. Assessing data available to populate the model, poor data on the natural history, utility and cost associated to PPMS were available and assumption or expert opinions will be needed to overcome the lack of robust data.CONCLUSIONS:A decision analytical model for PPMS can use a similar structure used in the models for RRMS. However, more robust data on PPMS and some structural change are needed to provide a good tool to assess cost-effectiveness of DMTS in PPMS.

Author(s):  
Mohammed Alam

Background: A decision analytical model investigating cost-effectiveness of Erlotinib was submitted to the UK NICE (National Institute for Health and Care Excellence), which was not based on actual health-state transition probabilities, leading to structural uncertainty in the model. The study adopted a Markov state-transition model for investigating the cost-effectiveness of Erlotinib versus Best Supportive Care (BSC) as a maintenance therapy for patients with non-small cell lung cancer (NSCLC). Methods: Unlike manufacturer submission (MS), the Markov model was governed by transition probabilities, and allowed a negative post-progression survival (PPS) estimate to appear in later cycle. Using published summary survival data, the study employs three fixed- and time-varying approaches to estimate state transition probabilities that are used in a restructured model. Results: Post-progression probabilities and probabilities of death for Erlotinib were different than fixed-transition approaches. The best fitting curves are achieved for both PPS and probability of death across the time for which data were available, but the curves start diverging towards the end of this period. The Markov model which extrapolates the curves forward in time suggests that this difference between a time-varying and fixed-transition becomes even greater. Our models produce an ICER of £54k -£66k per QALY gain, which is comparable to an ICER presented in the MS (£55k/QALY gain). Conclusions: Results from restructured Markov models show robust cost-effectiveness results for Erlotinib vs BSC. Although these are comparable to manufacturer submissions, in terms of magnitude, they vary, and which are crucial for interventions falling near a threshold value. The study will further explore the cost-effectiveness of therapies for NSCLC in Qatar.


Author(s):  
Amir Hashemi-Meshkini ◽  
Hedieh Sadat Zekri ◽  
Hasan Karimi-Yazdi ◽  
Pardis Zaboli ◽  
Mohammad Ali Sahraian ◽  
...  

Background: Pegylated (PEG) interferon beta 1a has been approved by the United States Food and Drug Administration (USFDA) as an alternative to interferon beta 1a for multiple sclerosis (MS). Due to its higher price, this study aimed to evaluate the cost-effectiveness of PEG-interferon beta 1-a compared with interferon beta 1a from an Iranian payer perspective. Methods: A Markov model was designed according to health states based on Expanded Disability Status Scale (EDSS) and one-month cycles over a 10-year time horizon. Direct medical and non-medical costs were included from a payer perspective. Results: The incremental cost-effectiveness ratio (ICER) was estimated around 11111 US dollars (USD) per quality-adjusted life-year (QALY) gained for the PEG-interferon versus interferon regimen [with currency rate of 29,000 Iranian Rial (IRR) to 1 USD in 2016]. Conclusion: Considering the cost-effectiveness  threshold in Iran [three times of gross domestic product (GDP) per capita or 15,945 USD], PEG-interferon beta 1-a could be considered as a cost effective treatment for Iranian patients with MS.


2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Zhenhua Wang ◽  
Qiang Zhang ◽  
Bin Wu

Objective. This study constructs, calibrates, and verifies a mathematical simulation model designed to project the natural history of ESCC and is intended to serve as a platform for testing the benefits and cost-effectiveness of primary and secondary ESCC prevention alternatives.Methods. The mathematical model illustrates the natural history of ESCC as a sequence of transitions among health states, including the primary health states (e.g., normal mucosa, precancerous lesions, and undetected and detected cancer). Using established calibration approaches, the parameter sets related to progression rates between health states were optimized to lead the model outputs to match the observed data (specifically, the prevalence of precancerous lesions and incidence of ESCC from the published literature in Chinese high-risk regions). As illustrative examples of clinical and policy application, the calibrated and validated model retrospectively simulate the potential benefit of two reported ESCC screening programs.Results. Nearly 1,000 good-fitting parameter sets were identified from 1,000,000 simulated sets. Model outcomes had sufficient calibration fit to the calibration targets. Additionally, the verification analyses showed reasonable external consistency between the model-predicted effectiveness of ESCC screening and the reported data from clinical trials.Conclusions. This parameterized mathematical model offers a tool for future research investigating benefits, costs, and cost-effectiveness related to ESCC prevention and treatment.


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