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Stratification of treatment by disease severity measures – an application to treatment for sleep apnoea

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Health Economics @ Cambridge welcome Claire Simons from the MRC Biostatistics Unit, University of Cambridge. In her talk, she will talk about the value of providing the right treatment for each sub-group of the population and the nature and challenges of such a stratification. Drawing on a case study of treatment for sleep apnoea, her work explores issues in the potential stratification of treatment decision by a continuous measure of disease severity.

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Results from a cost-effectiveness analysis show whether an intervention is cost-effective “on average” for the population studied. This can lead to a sub-optimal treatment being recommended for some groups in the population. Exploration of cost-effectiveness within subgroups can lead to a more efficient allocation of resources. Despite the obvious benefits of stratifying treatment and the National Institute of Health and Care Excellence (NICE) recommending patient subgroups should be explored, this is not regularly implemented.

This work explores issues in the potential stratification of treatment decision by a continuous measure of disease severity in a case study of treatment for sleep apnoea. This includes the selection of appropriate data, modelling how treatment effects vary with severity, full quantification of uncertainty, and efficient calculation of the stratified decision.

Literature on the use of Mandibular Advancement Devices (MADs) and Continuous Positive Airway Pressure (CPAP) as treatments for sleep apnoea were reviewed. Specifically, evidence was obtained on the Epworth Sleepiness score (ESS), a measure of disease severity, and the impact of treatment on this. Both aggregate and easily accessible individual participant data from the RCTs were obtained, and combined in a series of Bayesian model-based meta-regressions indicating more severe disease, as indicated by higher baseline ESS , is related to a higher absolute treatment effect. However, the strength of this relationship differs between the two treatments.

The parameter estimates and posterior distributions from these meta-regressions have been included in a cost-effectiveness analysis to quantify the benefit in stratifying the treatment decision. Using a regression approximation, we can determine the optimal treatment decision given any value of the continuous stratifying variable without re-running the cost-effectiveness model. There is some evidence that MAD is the optimal treatment for those with lower ESS values and CPAP for those with higher ESS . Value of Information ideas have been used to quantify the economic benefits of stratification and potential research to improve stratified decision-making finding benefit in collecting information on specific populations and using particular study designs.

This talk is part of the Health Economics @ Cambridge series.

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