By Esha Senchaudhuri
Thank you to Ursula Garczarek, Maria Costa, Michael Colopy, Shahrul Mt-Isa, Gaelle Saint-Hillary for your comments and discussion.
Encouragement from regulatory bodies has marshalled those engaged in pharmaceutical drug development to find new and innovative ways to utilize patient insight throughout the clinical development process. A particular priority has been to find novel methods of incorporating patient preferences into risk-benefit analysis. Several Cytel statisticians have joined a Benefit-Risk Special Interest Group (SIG) which, among other topics, has been focusing on improving and generalizing methodologies of preference elicitation in the pharmaceutical industry. After a highly successful workshop at last year’s PSI conference, the group intends to consolidate efforts once more through a series of events in the autumn and throughout 2019.
For some time now, the use of patient preferences in clinical development has occurred in conjunction with preclinical studies that intend to gauge endpoints of importance to patients, or post-market studies that aim to shape marketing strategy [1]. A more recent phenomenon, encouraged by the European Medicines Agency, involves incorporating patient preferences into the regulatory review process [2]. The 21st Century Cures Act performs a similar analysis, encouraging the development of “methodological approaches that may be used to develop and identify what is most important to patients with respect to burden of disease, burden of treatment, and the benefits and risks in the management of the patient’s disease” [3]. The manner in which such preferences ought to be included in review is a subject of much discussion, but there is strong support for including patient preferences into measurements of quantitative benefit-risk assessment [4].
The move expands the idea of benefit and risk to reflect a broader range of private values. Imagine that a side-effect of a new spinal therapy makes a person bedridden for two weeks. A different therapy creates several months of moderate pain. It might seem to a medical professional that two weeks of immobility is clinically preferable over the alternative, but for numerous reasons, a patient might view the long term moderate pain as less impactful on their quality of life. Some patients might have small children, others demanding employers. Further, even when restricted to purely medical side-effects, patients might have genuine disagreements about which side-effects are worse. This is in no small part due to their individual experience and life circumstances. In such cases, patient studies can identify patients who belong to subgroups with different preferences.
This presents a new challenge for pharmaceutical statisticians. In order to infer population-level knowledge from individuals and aggregate preferences about risks and benefits, statisticians need to determine how to assess individual preferences across multiple criteria as well as how to aggregate such complex individual preferences into measures useful for public debate and accountability for healthcare decisions. These two goals motivate the burgeoning field of preference elicitation.
The first task of preference elicitation, that of assessing preferences across a wider range of criteria, has led pharmaceutical statisticians to adopt methodologies from multi-criteria decision analysis for purposes of risk-benefit analysis. These methodologies, already familiar in a variety of industries, allow statisticians to quantify preferences informed by many different factors, for example levels of toxicity and duration of a side-effect, or efficacy in reaching different endpoints [1][2]. These approaches can capture a variety of different considerations, from pairwise comparison of alternatives to composite functions that capture preferences developed over many variables [1].
Translating various individual preferences into population-level preferences presents yet another statistical challenge. The goal of quantifying such preferences lends itself to many different forms of aggregation, each requiring rigorous experimental designs and sophisticated statistical models. It is important to bear in mind, of course, that the objective of such aggregation might be amenable to different forms of presentation. Sometimes, it is enough to know that a variety of therapeutic options ought to be available to patients, while in other scenarios, regulators may need to be aware that certain sub-populations would benefit from access to a particular new medicine.
Working through these questions while popularizing methods for preference elicitation is an important part of the mission of the Benefit-Risk Special Interest Group (SIG). Its initial workshop on preference elicitation at this year’s PSI conference, which took place in Amsterdam between the 3rd and 6th of June, has received overwhelmingly positive reviews from its participants, mainly clinical statisticians from across the spectrum of industry. As a testament to the growing need for such workshops, 22 out of 26 participants said that while they did not use preference elicitation methods daily, they had reasons to participate in the workshop ranging from expected future use to interest in the quantitative methodology.
The next SIG webinar on Preference Elicitation Methods will be held on November 20th, 15:00 - 16:30 UK Time. Click here for registration details.
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References
[1] Postmus, D., Mavris, M., Hillege, H.L., Salmonson, T., Ryll, B., Plate, A., Moulon, I., Eichler, H.G., Bere, N. and Pignatti, F., 2016. Incorporating patient preferences into drug development and regulatory decision making: Results from a quantitative pilot study with cancer patients, carers, and regulators. Clinical Pharmacology & Therapeutics, 99(5), pp.548-554.
[2] Postmus, D., Richard, S., Bere, N., van Valkenhoef, G., Galinsky, J., Low, E., Moulon, I., Mavris, M., Salmonsson, T., Flores, B. and Hillege, H., 2018. Individual Trade‐Offs Between Possible Benefits and Risks of Cancer Treatments: Results from a Stated Preference Study with Patients with Multiple Myeloma. The oncologist, 23(1), pp.44-51.
[3] 21st century Cures Act section 3002 https://www.fda.gov/downloads/forindustry/userfees/prescriptiondruguserfee/ucm563618.pdf
[4] Thokala, P., Devlin, N., Marsh, K., Baltussen, R., Boysen, M., Kalo, Z., Longrenn, T., Mussen, F., Peacock, S., Watkins, J. and Ijzerman, M., 2016. Multiple criteria decision analysis for health care decision making—an introduction: report 1 of the ISPOR MCDA Emerging Good Practices Task Force. Value in health, 19(1), pp.1-13.
