Overview of cost-effectiveness analysis and health state utilities in multiple myeloma and estimations of health state utilities from real-world macedonian data

Vjollca Qerimi,Sonja Genadieva-Stavric,Zoran Sterjev,Aleksandra Kapedanovska Nestorovska,Ljubica Suturkova

doi:10.33320/maced.pharm.bull.2016.62.02.003

Abstract

Multiple myeloma (MM) is a hematological neoplastic plasma-cell disorder in the bone marrow. Nowadays, the survival of MM patients has improved by using novel therapies (e.g., bortezomib, thalidomide). Decision-analytic models (DAM) are known to be very useful in guiding clinical and health policy decisions by systematically evaluating expected outcomes of alternative treatments. When conducting DAM, it is recommended by National Institute for Clinical Excellence (NICE) as the preferred framework to use qualityadjusted life years as the main measure for health outcomes derived from generic preference-based instruments. The objective of our study was to identify and analyze the structural and methodological approaches of published studies that evaluated: (1) DAM for various treatment strategies in MM and (2) published health utility values (HUVs) after MM treatment. The searches were developed in the electronic literature database PubMed/MEDLINE. Additionally, we aimed to derive real-world data for HUVs from the current patients’ treatment landscape in the Hematology Clinical Center in Skopje. The survey collecting national HUVs was conducted during the period January-November 2016, using the generic preference-based instrument EQ-5D-5L, administered at one-time point in 20 MM patients. Only five studies reported on using a DAM. Seven studies were included in the extraction of already published HUVs. The calculated national health utility score of 0.723 was comparable to the published data in the previously identified studies reporting on similar MM treatment protocols. Several modeling approaches and HUVs, developed for different treatment strategies and target groups in MM were identified. Those data could be used to parameterize a DAM for MM treatment.

Highlights

Multiple myeloma (MM) is a hematological neoplastic plasma cell disorder characterized by clonal proliferationVjollca Qerimi, Aleksandra Kapedanovska Nestorovska, Zoran Sterjev, Sonja Genadieva-Stavric, Ljubica Suturkova70 years, where 37% of patients are younger than 65 years, and the rest are aged above 65 years (Howlade, Noone and Krapcho, 2011; Palumbo and Anderson, 2011).When considering the quality-of-life impact and life expectancy, the MM is considered as a severe disease and there is still no cure
Even though decision trees can be developed easy, structured and modified without too many efforts, they can have several limitations when used for longer time horizons; as defined based on the ISPORSMDM Task Forces: “they are most suitable when the outcome set is small and defined, the time horizon is short, or when the consequences of a decision are known with some certainty“ (Roberts et al, 2012)
One can observe that different modeling studies that measure treatment effects and costs of MM are available; emphasizing the importance of decision-analytic modeling in assisting health policy decision making when considering treatment of MM patients

Summary

Introduction

Multiple myeloma (MM) is a hematological neoplastic plasma cell disorder characterized by clonal proliferationVjollca Qerimi, Aleksandra Kapedanovska Nestorovska, Zoran Sterjev, Sonja Genadieva-Stavric, Ljubica Suturkova70 years, where 37% of patients are younger than 65 years, and the rest are aged above 65 years (Howlade, Noone and Krapcho, 2011; Palumbo and Anderson, 2011).When considering the quality-of-life impact and life expectancy, the MM is considered as a severe disease and there is still no cure. With the new treatment regimens, patients are expected to experience different adverse drug events (such as: anemia, thrombocytopenia, neutropenia, lymphopenia, neuralgia, neuropathy, pneumonia, constipation, diarrhea, fatigue, nausea/vomiting, deep-vein thrombosis, etc.) that could lead to higher costs (Blade et al, 1998; Moreau et al, 2013; Moreau and Touzeau, 2015; Palumbo et al, 2011; Palumbo et al, 2014; Stojanoski et al, 2013)

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