Exploring Heterogeneity in Histology-Independent Technologies and the Implications for Cost-Effectiveness

Background The National Institute for Health and Care Excellence and a number of international health technology assessment agencies have recently undertaken appraisals of histology-independent technologies (HITs). A strong and untested assumption inherent in the submissions included identical clinical response across all tumour histologies, including new histologies unrepresented in the trial. Challenging this assumption and exploring the potential for heterogeneity has the potential to impact upon cost-effectiveness. Method Using published response data for a HIT, a Bayesian hierarchical model (BHM) was used to identify heterogeneity in response and to estimate the probability of response for each histology included in single-arm studies, which informed the submission for the HIT, larotrectinib. The probability of response for a new histology was estimated. Results were inputted into a simplified response-based economic model using hypothetical parameters. Histology-independent and histology-specific incremental cost-effectiveness ratios accounting for heterogeneity were generated. Results The results of the BHM show considerable heterogeneity in response rates across histologies. The predicted probability of response estimated by the BHM is 60.9% (95% credible interval 16.0; 91.8%), lower than the naively pooled probability of 74.5%. A mean response probability of 56.9% (0.2; 99.9%) is predicted for an unrepresented histology. Based on the economic analysis, the probability of the hypothetical HIT being cost-effective under the assumption of identical response is 78%. Allowing for heterogeneity, the probability of various approval decisions being cost-effective ranges from 93% to 11%. Conclusions Central to the challenge of reimbursement of HITs is the potential for heterogeneity. This study illustrates how heterogeneity in clinical effectiveness can result in highly variable and uncertain estimates of cost-effectiveness. This analysis can help improve understanding of the consequences of histology-independent versus histology-specific decisions.