Application of Cost–Effectiveness Analysis to Demonstrate the Potential value of Companion Diagnostics in Chronic Myeloid Leukemia

Gaultney, J; Sanhueza, Enrique; Janssen, Jeroen; Redekop, William K.; Groot, Carin A Uyl-de

doi:10.2217/pgs.10.187

Cited by 13 publications

(38 citation statements)

References 40 publications

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“…Expensive cancer drugs are increasingly developed for patient populations stratified by genetic characteristics and this trend illustrates an increasing role for biochemical, histological, and genetic markers to aid treatment decisions [ 35 ]. While the PHAROS registry focused on expensive drugs, registries may also be used to collect information on biochemical, histological, and genetic markers, which can be used for economic evaluations of these markers.…”

Section: Further Research Areas For Registry Datamentioning

confidence: 99%

A Practical Guide for Using Registry Data to Inform Decisions About the Cost Effectiveness of New Cancer Drugs: Lessons Learned from the PHAROS Registry

2015

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Decision makers increasingly request evidence on the real-world cost effectiveness of a new treatment. There is, however, a lack of practical guidance on how to conduct an economic evaluation based on registry data and how this evidence can be used in actual decision making. This paper explains the required steps on how to perform a sound economic evaluation using examples from an economic evaluation conducted with real-world data from the Dutch Population based HAematological Registry for Observational Studies. There are three main issues related to using registry data: confounding by indication, missing data, and insufficient numbers of (comparable) patients. If encountered, it is crucial to accurately deal with these issues to maximize the internal validity and generalizability of the outcomes and their value to decision makers. Multivariate regression modeling, propensity score matching, and data synthesis are well-established methods to deal with confounding. Multiple imputation methods should be used in cases where data are missing at random. Furthermore, it is important to base the incremental cost-effectiveness ratio of a new treatment compared with its alternative on comparable groups of (matched) patients, even if matching results in a small analytical population. Unmatched real-world data provide insights into the costs and effects of a treatment in a real-world setting. Decision makers should realize that real-world evidence provides extremely valuable and relevant policy information, but needs to be assessed differently compared with evidence derived from a randomized clinical trial.

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Section: Further Research Areas For Registry Datamentioning

confidence: 99%

A Practical Guide for Using Registry Data to Inform Decisions About the Cost Effectiveness of New Cancer Drugs: Lessons Learned from the PHAROS Registry

2015

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“…(21,(25)(26)(27)(28). Literature reviews also provided insight when a small amount of data was available (29,30). The theoretical studies highlighted case studies with subsequent economic modelling as an applicable approach to collect and analyse data (31,32).…”

Section: Methods (Qualitative or Quantitative)mentioning

confidence: 99%

“…To account for the dynamic characteristics of an early innovation, future technological development, organisational change, and learning curves should be incorporated into the models (42,43). Studies have pointed to the use of sensitivity analysis to deal with uncertainty in the interpretation of results and to test the impact of different implementation strategies when the technology is still dynamic (18,22,28,30,42,44). Constructive technology assessment that takes into account the learning curve seems to be appropriate in the early assessment of technologies that are still dynamic (23,26,(45)(46)(47).…”

Section: Evidence Gaps and Uncertainty In Early Economic Modellingmentioning

confidence: 99%

Early assessment of innovation in a healthcare setting

Støme

Moger

Kidholm

et al. 2019

Int J Technol Assess Health Care

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ObjectivesEarly assessment can assist in allocating resources for innovation effectively and produce the most beneficial technology for an institution. The aim of the present study was to identify methods and discuss the analytical approaches applied for the early assessment of innovation in a healthcare setting.MethodsKnowledge synthesis based on a structured search (using the MEDLINE, Embase, and Cochrane databases) and thematic analysis was conducted. An analytical framework based on the stage of innovation (developmental, introduction, or early diffusion) was applied to assess whether methods vary according to stage. Themes (type of innovation, study, analysis, study design, method, and main target audience) were then decided among the authors. Identified methods and analysis were discussed according to the innovation stage.ResultsA total of 1,064 articles matched the search strategy. Overall, thirty-nine articles matched the inclusion criteria. The use of methods has a tendency to change according to the stage of innovation. Stakeholder analysis was a prominent method in the innovation stages and particularly in the developmental stage, as the introduction and early diffusion stage has more availability of data and may apply more complex methods. Barriers to the identified methods were also discussed as all of the innovation stages suffered from lack of data and substantial uncertainty.ConclusionsAlthough this review has identified applicable approaches for early assessment in different innovation stages, research is required regarding the value of the available data and methods and tools to enhance interactions between different parties at different stages of innovation.

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“…Companion diagnostic tests also can help to predict which of the available drugs will be of greatest benefit to an individual patient. An example of this kind of test can be found in secondline therapy of chronic myeloid leukemia, where the effectiveness of candidate treatments such as dasatinib and nilotinib may be predicted before initiating treatment [14].…”

Section: Clinical Question Type Of Technology Medical Decisionmentioning

confidence: 99%

The Faces of Personalized Medicine: A Framework for Understanding Its Meaning and Scope

2013

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The objective of this article was to provide a framework for understanding the different definitions of the term "personalized medicine." The term personalized medicine is used regularly but interpreted in different ways. This article approaches the term by starting with a broad view of clinical medicine, where three components can be distinguished: the questions (e.g., what is the diagnosis?), the methods used to answer them (e.g., a test), and the available actions (e.g., to give or not give a particular drug). Existing definitions of personalized medicine disagree about which questions, methods, and actions fall within its domain. Some define the term narrowly, referring to the use of a diagnostic test to predict drug response, thereby clarifying whether or not a patient will benefit from that drug. An example of this combination is the HER2/neu test to predict the effectiveness of trastuzumab in breast cancer. Many who adopt this definition associate the concept of personalized medicine with fields such as genetics, genomics, and other types of "-omics." In contrast, others view personalized medicine as a concept that has always existed, because medicine has always considered the needs of the individual. One definition of personalized medicine that accommodates both interpretations is "the use of combined knowledge (genetic or otherwise) about a person to predict disease susceptibility, disease prognosis, or treatment response and thereby improve that person's health." This predictive ability can increase over time through innovations in various technologies, resulting in further improvements in health outcomes. Moreover, these developments can lead to a better understanding of the underlying causes of disease, which can eventually lead to breakthroughs in the treatment of individual patients. In that sense, a truly personalized form of medicine can also be seen as an ideal, a goal that will be achieved only after multiple advances in science. Although the term personalized medicine was rechristened somewhat recently, our ability to personalize medicine will continue to advance in unimaginable ways as we come to learn more about the heterogeneity that exists among individuals and diseases.

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Application of Cost–Effectiveness Analysis to Demonstrate the Potential value of Companion Diagnostics in Chronic Myeloid Leukemia

Abstract: This case demonstrates the use of cost-effectiveness analysis at an early stage of health technology assessment to generate economic evidence for the use of companion diagnostics in treatment decisions and to support decision-making for their development.

Cited by 13 publications

References 40 publications

A Practical Guide for Using Registry Data to Inform Decisions About the Cost Effectiveness of New Cancer Drugs: Lessons Learned from the PHAROS Registry

A Practical Guide for Using Registry Data to Inform Decisions About the Cost Effectiveness of New Cancer Drugs: Lessons Learned from the PHAROS Registry

Early assessment of innovation in a healthcare setting

The Faces of Personalized Medicine: A Framework for Understanding Its Meaning and Scope

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