Policy and Statistical Issues in HTA Review of Histology-Independent Technologies (HIT) in Oncology
Discussion Leader: Jeremy Williams Snider, PhD, Flatiron Health, New York, NY, USA
Discussants: Jacoline Bouvy, PhD, Science Policy and Research Programme, National Institute for Health and Care Excellence, London, UK; Joshua Ray, MSc, Global Access, F. Hoffmann-La Roche, Basel, BS, Switzerland
PURPOSE: This workshop will provide an overview of issues identified by the recent Framework for the Health Technology Assessment of Histology‑independent Precision Oncology Therapies (Gaultney, 2021), and discuss statistical methods and sources of evidence to address these issues.
DESCRIPTION: The National Institute for Health and Care Excellence (NICE) and other HTA Agencies have started to evaluate the use of HITs in precision oncology. Assessment of the clinical and economic benefits of such treatments can be complicated by the heterogeneity of patient populations covered by a histology-independent approval, as well as the types of evidence that are commonly generated for a technology approval (and thus are available for HTA review). Failure to adequately account for heterogeneity in cost-effectiveness across histologies may result in the reimbursement of a HIT for histologies in which it is not cost-effective, and likewise, failure to reimburse in histologies where it may be cost-effective. Inclusive Phase II trials for HIT indications can often fail to generate needed evidence for HTA review as they are unable to capture all possible histologies that are eligible for the technology, unable to gather sufficient numbers of patients within individual histologies, and are uncontrolled. As new agents are identified and evaluated, and post-approval data become available, analyses incorporating real-world evidence can supplement trials and aid in decision-making that can provide insight into subpopulation heterogeneity, particularly as it relates to quantifying the benefits of introducing a HIT into standard practice. AUDIENCE INTERACTIVE ELEMENTS Polls will assess prior participant HTA engagement, as well as experience with various HIT modelling approaches. Simulated histology-agnostic longitudinal datasets and related R code will assist in understanding how multilevel models can estimate prognostic and predictive value of HIT-relevant biomarkers, effectively quantify histology-specific uncertainty, and identify areas where additional information can have highest impact in an HTA submission.
Conference/Value in Health Info