Probability Elicitation to Inform Early Health Economic Evaluations of New Medical Technologies: A Case Study in Heart Failure Disease Management

Cao, Qi; Postmus, Douwe; Hillege, Hans L.; Buskens, Erik

doi:10.1016/j.jval.2013.02.008

Cited by 25 publications

(41 citation statements)

References 30 publications

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“…Belief elicitation is used in quite a number of papers implementing headroom analysis or an early-stage health economic model. Specific applications of elicitation methods were published in several studies presenting an early health economic model [44–46] or for estimation of headroom [24, 47]. …”

Section: Methods For Early Health Technology Assessmentmentioning

confidence: 99%

“…Cao et al and Kip et al used probability elicitation to estimate transition probabilities in a health economic model [47]. Haakma et al used belief elicitation to estimate the likely diagnostic performance of a new imaging device [44].…”

Section: Methods For Early Health Technology Assessmentmentioning

confidence: 99%

“…2013 Jun; 16(4):529–35. [47]22015Chapman AM, Taylor CA, Girling AJ. Early HTA to Inform Medical Device Development Decisions - The Headroom Method.…”

Section: Appendixmentioning

confidence: 99%

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Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature

et al. 2017

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Early health technology assessment is increasingly being used to support health economic evidence development during early stages of clinical research. Such early models can be used to inform research and development about the design and management of new medical technologies to mitigate the risks, perceived by industry and the public sector, associated with market access and reimbursement. Over the past 25 years it has been suggested that health economic evaluation in the early stages may benefit the development and diffusion of medical products. Early health technology assessment has been suggested in the context of iterative economic evaluation alongside phase I and II clinical research to inform clinical trial design, market access, and pricing. In addition, performing early health technology assessment was also proposed at an even earlier stage for managing technology portfolios. This scoping review suggests a generally accepted definition of early health technology assessment to be “all methods used to inform industry and other stakeholders about the potential value of new medical products in development, including methods to quantify and manage uncertainty”. The present review also aimed to identify recent published empirical studies employing an early-stage assessment of a medical product. With most included studies carried out to support a market launch, the dominant methodology was early health economic modeling. Further methodological development is required, in particular, by combining systems engineering and health economics to manage uncertainty in medical product portfolios.

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Section: Methods For Early Health Technology Assessmentmentioning

confidence: 99%

Section: Methods For Early Health Technology Assessmentmentioning

confidence: 99%

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Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature

et al. 2017

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“…An article was excluded if (a) the content was actually theoretical and there was no application, (b) it was a review with no new content, (c) multistate models were referenced, flagging it for review but the models were not actually fit, or (d) the models were actually discrete-time. Of the remaining 26 articles, 25 fit models to data with five or fewer states and two fit models to data with six states (Aalen, 2012; Alessandrino et al, 2013; Allen & Farewell, 2009; Batina et al, 2016; Cao et al, 2013; Chauvel et al, 2007; Chui, 2002; Combescure et al, 2003; Elbasha et al, 2009; Gangnon et al, 2012; Garcia et al, 2016; Haeussler et al, 2016; Hanly et al, 2016; Jackson et al, 2012; Jambarsang et al, 2015; Joutard et al, 2012; Liu et al, 2003; Ndumbi et al, 2013; Nunez et al, 2016; Raiche et al, 2012, 2014; Rodriguez-Girondo & de Una-Alvarez, 2012; Saint-Pierre et al, 2006; Tung et al, 2006; Zhang et al, 2014). One publication used a six-state model to analyze a dataset with much more data than is typically collected in acute stroke trials- approximately 5,000 patients (Gangnon et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Cassarly

Martin

Chimowitz

et al. 2017

Communications in Statistics - Simulation and Computation

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Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.

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“…In addition, such evidence may be used to inform the potential cost‐effectiveness of a new test and the drivers of diagnostic performance. One way to deal with this would be to perform a model‐based early health technology assessment where part of the evidence is determined by expert elicitations . Elicitation involves a process by which experts formulate a quantitative judgment based on their own beliefs, independent of the quality of such knowledge, for an uncertain quantity .…”

Section: Introductionmentioning

confidence: 99%

Using expert elicitation to estimate the potential impact of improved diagnostic performance of laboratory tests: a case study on rapid discharge of suspected non–ST elevation myocardial infarction patients

Kip

Steuten

Koffijberg

et al. 2016

Evaluation Clinical Practice

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Early health technology assessment can provide insight in the potential cost-effectiveness of new tests to guide further development decisions. This can increase their potential benefit but often requires evidence which is lacking in early test development stages. Then, expert elicitation may be used to generate evidence on the impact of tests on patient management. This is illustrated in a case study on a new triple biomarker test (copeptin, heart-type fatty acid binding protein, and high-sensitivity troponin [HsTn]) at hospital admission. The elicited evidence enables estimation of the impact of using the triple biomarker on time to exclusion of non-ST elevation myocardial infarction compared with current serial HsTn measurement (performed 0, 2, and 6 h after admission). Cardiologists were asked to estimate the effect of the triple biomarker on patient's discharge rates and interventions performed, depending on its diagnostic performance. This elicited evidence was combined with Dutch reimbursement data and published evidence into a decision analytic model. Direct hospital costs and patients' discharge rates were assessed for 3 testing strategies including this triple biomarker (ie, only at admission or combined with HsTn measurements after 2 and 6 h). Direct hospital costs of suspected non-ST elevation myocardial infarction patients using serial HsTn measurements are estimated at €1825 per patient. Combining this triple biomarker with HsTn measurements after 2 and 6 hours is expected to be the most cost-effective strategy. Depending on the diagnostic performance of the triple biomarker, this strategy is estimated to reduce costs with €66 to €205 per patient (ie, 3.6%-11.3% reduction). Expert elicitation can be a valuable tool for early health technology assessment to provide an initial estimate of the cost-effectiveness of new tests prior to their implementation in clinical practice. As demonstrated in our case study, improved diagnostic performance of the triple biomarker may have benefits that should be further explored.

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Probability Elicitation to Inform Early Health Economic Evaluations of New Medical Technologies: A Case Study in Heart Failure Disease Management

Cited by 25 publications

References 30 publications

Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature

Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Using expert elicitation to estimate the potential impact of improved diagnostic performance of laboratory tests: a case study on rapid discharge of suspected non–ST elevation myocardial infarction patients

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