Open Access: Informed decision-making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessments

Every week, we select a recently published Open Access article to feature. This week’s article is from Pharmaceutical Statistics and evaluates decision making in health technology assessment surrogacy.  

The article’s abstract is given below, with the full article available to read here. 

Weir, CJTaylor, RSInformed decision-making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessmentsPharmaceutical Statistics2022214): 740– 756. doi:10.1002/pst.2219

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.

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