Informed decision‐making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessments. (12th July 2022)

• Record Type: Journal Article
• Authors: Weir, Christopher J.; Taylor, Rod S.
• Other Names: Senn Stephen guestEditor.; Smith Mike K. guestEditor.
• Is Part Of: Pharmaceutical statistics. Volume 21:Number 4(2022)
• Page Start: 740
• Page End: 756
• Publication Date: 2022-07-12
• Subjects: licensing -- reimbursement -- surrogate outcome; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2219
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract: The desire, by patients and society, for faster access to therapies has driven a long tradition of the use of surrogate endpoints in the evaluation of pharmaceuticals and, more recently, biologics and other innovative medical technologies. The consequent need for statistical validation of potential surrogate outcome measures is a prime example on the theme of statistical support for decision‐making in health technology assessment (HTA). Following the pioneering methodology based on hypothesis testing that Prentice presented in 1989, a host of further methods, both frequentist and Bayesian, have been developed to enable the value of a putative surrogate outcome to be determined. This rich methodological seam has generated practical methods for surrogate evaluation, the most recent of which are based on the principles of information theory and bring together ideas from the causal effects and causal association paradigms. Following our synopsis of statistical methods, we then consider how regulatory authorities (on licensing) and payer and HTA agencies (on reimbursement) use clinical trial evidence based on surrogate outcomes. We review existing HTA surrogate outcome evaluative frameworks. We conclude with recommendations for further steps: (1) prioritisation by regulators and payers of the application of formal surrogate outcome evaluative frameworks, (2) application of formal Bayesian decision‐analytic methods to support reimbursement decisions, and (3) greater utilization of conditional surrogate‐based licensing and reimbursement approvals, with subsequent reassessment of treatments in confirmatory trials based on final patient‐relevant outcomes.