Wednesday, June 12, 2019

Novel Approaches to Complex Valuation (Notepad)

https://www.healthcare-economist.com/2019/06/10/what-is-mcda-and-why-do-we-need-it/


What is MCDA and why do we need it?

Jason ShafrinLeave a comment

Value measurement using cost-effectiveness analysis (CEA) is one of the core stables of health economic research.  CEA often uses quality adjusted life years (QALYs) to capture how a treatment affects a patients mortality and morbidity.  CEA makes explicit assumptions about the tradeoffs between mortality and morbidity by assuming these are additive.  Further, this approach, however, ignores much that may be important to patients including mode of administration, caregiver burden, risk preferences, and other factors. 
A 2019 paper by Charles Phelps and Guruprasad Madhavan argues that a better approach may be to incorporate the use of multi-criteria decision analysis (MCDA).  This approach—developed from systems engineering—measures how different treatments perform across a variety of attributes and explicitly asks the decision maker to weigh these different decisions.  I helped to create MCDA modules for the Innovation and Value Initiative’s IVI-RA and IVI-NSCLC Value Tools.  There are a variety of different types of MCDA approaches including: ELimination and Choice Expressing Reality (ELECTRE), Multi-Attribute Utility Theory (MAUT), Analytic Hierarchy Process (AHP), Measuring Attractiveness by a Categorical Evaluation Technique (MACBETH), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) and many others.
Phelps and Madhavan also describe how MCDA could be incorporated into health care decision-making. 
  • Conduct direct comparisons of MCDA and CEA to see where the approaches produce similar answers and where the approaches produce different answers.
  • Building the MCDA capability through more commonly available data resources, increased number of researchers with MCDA skills, increased validation of tools and improving human factor dimensions (e.g., ease of use, comprehensibility).
  • Understanding how different voting methods perform when used to implement MCDA models in group settings.....



Valuing Health: Evolution, Revolution, Resistance, and Reform

Charles E. Phelps, PhD, MBA1,∗,'Correspondence information about the author PhD, MBA Charles E. Phelps
,
Guruprasad Madhavan, PhD, MBA2

PlumX Metrics
DOI: https://doi.org/10.1016/j.jval.2019.01.010
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Abstract

A number of methods have sought to determine the value of interventions and services that promote health, even when no agreement exists on the proper way to determine and define “value.” Previous valuation efforts began simply by counting deaths or measuring life expectancy, slowly evolving to the widespread use of cost-effectiveness analysis (CEA) as the de facto normative standard for medical interventions. Users of CEA recognize that the method is incomplete. Further, no meaningful agreement exists on how best to apply CEA in decision settings because of either inadequacies in the CEA framework or lack of consensus on how to use it in a setting with budget constraints. Yet efforts to value health still predominantly use (and continue to recommend) this limited framework. Is this owing to a lack of new ideas and motivation, resistance to change, or an aversion to embrace more comprehensive systems approaches? We argue that tools of systems engineering can advance our capabilities, but they have had only limited use in health policy. We identify some reasons and specifically highlight the promise of systems-analytic platforms—such as multicriteria decision support systems—and the need to make them more accessible for different uses in real situations with real consequences. We also explore the need for comparative testing of different multicriteria approaches (including direct comparisons with CEA) to learn when and by how much the recommendations differ and what the consequences might be.



Combining Health and Outcomes Beyond Health in Complex Evaluations of Complex Interventions: Suggestions for Economic Evaluation

John Wildman, PhD1,∗,'Correspondence information about the author PhD John Wildman
,
Josephine M. Wildman, PhD2

PlumX Metrics
DOI: https://doi.org/10.1016/j.jval.2019.01.002
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Highlights

  • •
    The literature has called for comprehensive frameworks that can accommodate multiple attributes. This article considers the challenges of applying economic evaluation to complex interventions using the example of a novel health intervention based on the social model of health.
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    It has been suggested that multicriteria decision analysis and value frameworks lack theoretical underpinnings. We combine tools from health economics and economics, with theoretical underpinnings, to value health and outcomes or attributes beyond health in a common numeraire.
  • •
    We suggest that important attributes should be identified through mixed-methods approaches and valued at the population level.

Abstract

In this journal there has been considerable discussion regarding the development of tools for valuing the multiple attributes that arise from complex interventions with benefits beyond health. Nevertheless, unlike the rigorous underpinnings of cost-utility analysis, much of this work has been taking place in fragmented research communities and without theoretical underpinnings, leading to a call for better and more comprehensive frameworks. We discuss the challenges faced by economic evaluation using as our example a “social prescribing” intervention, a novel health intervention based on the social model of health. We suggest a mixed-methods approach to uncover important attributes and then combine tools from health economics and economics to provide measures of benefit in a common money numeraire. This approach provides the theoretical underpinnings necessary for deliberate, transparent, and structured decision-making processes. It also enables the correct allocation of costs within complex payment systems. We suggest that, because of the complexities of randomized controlled trials, interventions should be introduced in a way that allows the application of causal analysis for evaluation. In the short term, such evaluations may be challenging and expensive. Nevertheless, as has happened with health economics evaluation and the quality-adjusted life-year, when a common set of attributes is agreed upon, the expense will fall and these methods can become embedded in interventions with diffuse outcomes.

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Believe this is 2018 Abstract/ASCO/PENNELL


In 2018, PENNELL Abstract was also picked up as a news article in ASCO POST


Decision-Analytic Model Finds Upfront, Comprehensive Genetic Testing in Advanced Lung Cancer Is Cost-Efficient


By The ASCO Post
June 10, 2018
Get Permission
Nathan A. Pennell, MD, PhD
Nathan A. Pennell, MD, PhD
AN ECONOMIC model comparing different types of genetic testing in metastatic non–small cell lung cancer (NSCLC) found using next-generation sequencing to test for all known lung cancer–related gene changes at the time of diagnosis was less costly and faster than sequentially testing one or a limited number of genes at a time. According to the model, next-generation sequencing saved as much as $2.1 million for Medicare and more than $250,000 for commercial insurance providers. The study was presented at the 2018 ASCO Annual Meeting and was reported in a poster session by Nathan A. Pennell, MD, PhD, Co-Director of the Cleveland Clinic Lung Cancer Program, and colleagues.1 
“The field of lung cancer treatment is moving at a rapid pace, and we need to fully characterize genomic changes to determine the best available treatment for patients shortly after they are diagnosed,” stated Dr. Pennell. “Today......

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PENNELL 2019


Economic Impact of Next-Generation Sequencing Versus Single-Gene Testing to Detect Genomic Alterations in Metastatic Non–Small-Cell Lung Cancer Using a Decision Analytic Model

Nathan A. Pennell, MD, PhD1; Alex Mutebi, PhD2; Zheng-Yi Zhou, PhD3; Marie Louise Ricculli, MSc3; Wenxi Tang, MS3; Helen Wang3; ...Show More





PURPOSE
The aim of the current study was to assess the economic impact of using next-generation sequencing (NGS) versus single-gene testing strategies among patients with metastatic non–small-cell lung cancer (mNSCLC) from the perspective of the Centers for Medicare & Medicaid Services (CMS) and US commercial payers.

METHODS
A decision analytic model considered patients who were newly diagnosed with mNSCLC who received programmed death ligand 1 and genomic alteration tests—EGFR, ALK, ROS1, BRAF, MET, HER2, RET, and NTRK1—using upfront NGS (all alterations tested simultaneously plus KRAS), sequential testing (sequence of single-gene tests), exclusionary testing (KRAS plus sequential testing), and hotspot panels (EGFR, ALK, ROS1, and BRAF tested simultaneously plus single-gene tests or NGS for MET, HER2, RET, and NTRK1). Model outcomes for each strategy were time-to-test results, the proportion of patients identified harboring alterations with or without US Food and Drug Administration–approved therapies, and total testing costs. A budget impact analysis assessed the economic effects of increasing the proportion of NGS-tested patients.

RESULTS
In a hypothetical 1,000,000-member health plan, 2,066 Medicare-insured patients and 156 commercially insured patients were estimated to have mNSCLC and to be eligible for testing. Time-to-test results were 2.0 weeks for NGS and the hotspot panel, faster than exclusionary and sequential testing by 2.7 and 2.8 weeks, respectively. NGS was associated with cost savings for both CMS ($1,393,678; $1,530,869; and $2,140,795 less than exclusionary, sequential testing, and hotspot panels, respectively) and commercial payers ($3,809; $127,402; and $250,842 less than exclusionary, sequential testing, and hotspot panels, respectively). Increasing the proportion of NGS-tested patients translated into substantial cost savings for both CMS and commercial payers.

CONCLUSION
Use of upfront NGS testing in patients with mNSCLC was associated with substantial cost savings and shorter time-to-test results for both CMS and commercial payers.



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Around the PENNELL article, ASCO POST recommends these links, looks like all are 2018.

We recommend

  1. 2018 ASCO: Upfront, Comprehensive Genetic Testing in Advanced Lung Cancer Is Cost-Effective
    By The ASCO Post et al., Lung Cancer, 2018
  2. Decision-Analytic Model Finds Upfront, Comprehensive Genetic Testing in Advanced Lung Cancer Is Cost-EfficientRelated Articles
    By The ASCO Post et al., Lung Cancer, 2018
  3. ASCO 2018: Upfront, Comprehensive Genetic Testing in Advanced Lung Cancer Is Cost-Effective
    By The ASCO Post, The ASCO Post, 2018
  4. Nathan A. Pennell, MD, PhD, on NSCLC: Lowering Genetic Testing Costs
    Lung Cancer
  5. Expert Point of View: Bruce Johnson, MD, FASCORelated Articles
    By The ASCO Post et al., Lung Cancer, 2018
  1. Metastatic Lung Cancer: Emerging Therapeutic Strategies
    Sana Saif Ur Rehman et al., Medscape
  2. Predicine Closes $13.5M Series A Round
    360Dx, 2018
  3. Analysis of Durvalumab Consolidation Therapy Costs in Stage III NSCLC
    John Schieszer et al., Cancer Therapy Advisor, 2018
  4. Largest Liquid Biopsy Study: Compares With Tissue Sampling
    Kate Johnson et al., Medscape
  5. Veracyte, Johnson & Johnson Innovation Collaborate on Lung Cancer Dx
    360Dx, 2019






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