Towards Personalising the Use of Biologics in Rheumatoid Arthritis: A Discrete Choice Experiment

The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review

INTRODUCTION

Stakeholder preference evaluations are increasingly emphasized in healthcare policy and health technology assessment. Discrete choice experiments (DCEs) are the most common method for quantifying preferences among patients, the public, and healthcare professionals. While prior reviews (1990-2017) have examined DCE trends, no comprehensive synthesis exists for studies published since 2018. This updated review (2018-2023) provides critical insights into evolving methodologies and global trends in health-related DCEs.

METHODS

A systematic search (2018-2023) of Medline, Embase, and Web of Science identified relevant studies. Studies were screened for inclusion and data were extracted, including details on DCE design and analysis. To enable trend comparisons, the search strategy and extraction items aligned with previous reviews.

RESULTS

Of 2663 identified papers, 1279 met the inclusion criteria, reflecting a significant rise in published DCEs over time. DCEs were conducted globally, with a remarkable increase in publications from Asia and Africa compared with previous reviews. Experimental designs and econometric models have advanced, continuing prior trends. Notably, most recent DCEs were administered online.

DISCUSSION

The rapid growth of DCE applications underscores their importance in health research. While the methodology is advancing rapidly, it is crucial that researchers provide full transparency in reporting their methods, particularly in detailing experimental designs and validity tests, which are too often overlooked. Key recommendations include improving reporting of experimental designs, applying validity tests, following good practices for presenting benefit-risk attributes, and adopting open science practices. Ensuring methodological rigor will maximize the impact and reproducibility of DCE research in health economics.

Therapeutic Drug Monitoring of Biologics: Current Practice, Challenges and Opportunities - a Workshop Report

Therapeutic drug monitoring (TDM) for dose modification of biologics has the potential to improve patient outcomes. The US Food and Drug Administration (FDA) and the American Association of Pharmaceutical Scientists (AAPS) hosted the first US-based public workshop on TDM of biologics with contributions from a broad array of interested parties including healthcare providers, clinical pharmacologists, test developers, bioanalysis and immunogenicity scientists, health economics and outcomes research (HEOR) experts and regulators. The key insight was that despite a body of evidence to support TDM in certain therapeutic areas, there remain substantial challenges to widespread clinical implementation. There is a lack of consensus regarding the integration of TDM in clinical guidelines, and a lack of consensus on the cost-effectiveness of TDM; both factors contribute to the difficulty that healthcare providers face in obtaining reimbursement for TDM (both coverage of testing itself, and coverage of potential dosing modifications). The HEOR experts outlined alternative routes to obtaining reimbursement and suggested advocating for changes in coverage policies to promote TDM use in the clinic. Reaching alignment across policy makers, patients and advocacy groups, payers, and healthcare providers, on specific treatment settings where TDM will be clearly beneficial, was identified as an important step to advancing TDM implementation for the benefit of patients.

Measuring Patient Preferences to Inform Clinical Trial Design: An Example in Rheumatoid Arthritis

BACKGROUND

Allogeneic bone marrow transplantation (BMT) may be a curative treatment for patients with rheumatoid arthritis (RA), but it has serious risks, including death. It is uncertain whether patients would accept the risks and benefits of BMT and participate in clinical trials. We conducted a discrete choice experiment (DCE) to quantify risk tolerance and benefit-risk trade-offs to inform the design of a clinical trial for BMT.

METHODS

We conducted a DCE with three attributes (three levels each): chance of stopping disease progression (50-90%), increased chance of death in year after transplant (3-15%), and chance of chronic graft-versus-host disease (cGVHD) (3-15%). An orthogonal main effects design of nine binary choice tasks were presented for two scenarios: one considering their current situation and a second scenario where the patient has failed seven anti-rheumatic drugs. Participants were recruited from the Rheum4U inflammatory arthritis registry. Choice data were analyzed using a logit model accounting for multiple responses per participant.

RESULTS

Sixty patients participated. Most (82%) had severe disease, and the median number of anti-rheumatic drugs previously taken was 6 (range 0-18). As expected, an increased chance of stopping disease progression increases the probability of choosing BMT, while increased chance of both risks decreases the probability. Patients were willing to accept a 3% increase in risk of death or 6% increase in chance of chronic GVHD for a 10% increase in the chance of stopping disease progression. For the most clinically likely BMT risk-benefit profiles, and the likely initial target population of patients who have failed multiple biologics, between 72% and 91% of patients would choose BMT.

CONCLUSIONS

Patients with RA are willing to accept substantial risks for a chance to stop disease progression with BMT, suggesting that a pilot trial of BMT for RA could successfully recruit patients. Preference studies have an important role in informing patient-centered clinical trial planning and design.

Economics and Precision Medicine

Introducing precision medicine strategies into routine practice will require robust economic evidence. Decision-makers need to understand the value of a precision medicine strategy compared with alternative ways to treat patients. This chapter describes health economic analysis techniques that are needed to generate this evidence. The value of any precision medicine strategy can be demonstrated early to inform evidence generation and improve the likelihood of translation into routine practice. Advances in health economic analysis techniques are also explained and their relevance to precision medicine is highlighted. Ensuring that constraints on delivery are resolved to increase uptake and implementation will improve the value of a new precision medicine strategy. Empirical methods to quantify stakeholders' preferences can be effective to inform the design of a precision medicine intervention or service delivery model. A range of techniques to generate relevant economic evidence are now available to support the development and translation of precision medicine into routine practice. This economic evidence is essential to inform resource allocation decisions and will enable patients to benefit from cost-effective precision medicine strategies in the future.