Valuing Health Gain from Composite Response Endpoints for Multisystem Diseases

Objectives: This study aimed to demonstrate how to estimate the value of health gain after patients with a multisystem disease achieve a condition-specific composite response endpoint. Methods: Data from patients treated in routine practice with an exemplar multisystem disease (systemic lupus erythematosus) were extracted from a national register (British Isles Lupus Assessment Group Biologics Register). Two bespoke composite response endpoints (Major Clinical Response and Improvement) were developed in advance of this study. Difference-in-differences regression compared health utility values (3-level version of EQ-5D; UK tariff) over 6 months for responders and nonresponders. Bootstrapped regression estimated the incremental quality-adjusted life-years (QALYs), probability of QALY gain after achieving the response criteria, and population monetary benefit of response. Results: Within the sample (n = 171), 18.2% achieved Major Clinical Response and 49.1% achieved Improvement at 6 months. Incremental health utility values were 0.0923 for Major Clinical Response and 0.0454 for Improvement. Expected incremental QALY gain at 6 months was 0.020 for Major Clinical Response and 0.012 for Improvement. Probability of QALY gain after achieving the response criteria was 77.6% for Major Clinical Response and 72.7% for Improvement. Population monetary benefit of response was £1 106 458 for Major Clinical Response and £649 134 for Improvement. Conclusions: Bespoke composite response endpoints are becoming more common to measure treatment response for multisystem diseases in trials and observational studies. Health technology assessment agencies face a growing challenge to establish whether these endpoints correspond with improved health gain. Health utility values can generate this evidence to enhance the usefulness of composite response endpoints for health technology assessment, decision making, and economic evaluation

The Future of Precision Medicine : Potential Impacts for Health Technology Assessment

Objective Precision medicine allows health care interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information or treatment response. We analyse what developments are expected in precision medicine over the next decade and consider the implications for health technology assessment (HTA) agencies. Methods We perform a pragmatic review of the literature on the health economic challenges of precision medicine, and conduct interviews with representatives from HTA agencies, research councils and researchers from a variety of fields, including digital health, health informatics, health economics and primary care research. Results Three types of precision medicine are highlighted as likely to emerge in clinical practice and impact upon HTA agencies: complex algorithms, digital health applications and ‘omics’-based tests. Defining the scope of an evaluation, identifying and synthesizing the evidence and developing decision analytic models will more difficult when assessing more complex and uncertain treatment pathways. Stratification of patients will result in smaller subgroups, higher standard errors and greater decision uncertainty. Equity concerns may present in instances where biomarkers correlate with characteristics such as ethnicity, whilst fast-paced innovation may reduce the shelf-life of guidance and necessitate more frequent reviewing. Discussion Innovation in precision medicine promises substantial benefits to patients, but will also change the way in which some health services are delivered and evaluated. As biomarker discovery accelerates and AI-based technologies emerge, the technical expertise and processes of HTA agencies will need to adapt if the objective of value for money is to be maintained