DETERMINANTS OF INCREASING THE LIKELIHOOD FOR A POSITIVE DRUG REIMBURSEMENT RECOMMENDATION IN SCOTLAND
Author(s)
Pantiri K, Baeten S, Majer IM, Heeg B, Charokopou M
Pharmerit International, Rotterdam, The Netherlands
Presentation Documents
OBJECTIVES A binary reimbursement prediction model was previously developed based on a dataset of submissions to the Scottish Medicines Consortium (SMC) between 2006 and 2014. The objective of this study is to build on the previous model by identifying factors that influence the different levels of SMC recommendation, defined as “recommend”, “restrict” or “not recommend” pharmaceutical technologies for use in Scotland. METHODS Univariate and multivariate ordered logistic regression analyses were performed to assess the impact by means of odds ratios (OR) of the submitted evidence to the SMC on the decision. The proportional odds assumption underlying the current approach was tested. RESULTS Out of 463 applications, 115 received positive recommendation (25%), 150 received restricted recommendation (32%) and 198 (43%) were not recommended. Univariate analyses showed that 14 variables significantly affected the SMC decision. The multivariate analyses showed significant associations (p≤0.05) between the SMC decision and several variables, including: (1) a product demonstrating cost savings and QALY gains [OR=6.11], (2) a product not being cost-effective (ICER≥£30,000/QALY) [OR=0.50], (3) a non-superior efficacy outcome versus placebo [OR=0.15], (4) the product’s therapeutic indication (nervous system [OR=0.51], blood forming organs [OR=2.29]), (5) whether the product was indicated for non‑chronic use [OR=1.48] and (6) whether the submission was performed by a big company [OR=1.86]. The proportional odds assumption was not violated, proving the appropriateness of the current model. The present model yielded similar results with the previously developed binary logistic one, further ensuring face validity, yet this approach is considered to better fit the multidimensional nature of SMC’s decision and increase the predictive power of the model. CONCLUSIONS
This study identified superior efficacy using an active comparator as well as a beneficial cost-effectiveness outcome to increase the likelihood of receiving a positive recommendation by the SMC.
Conference/Value in Health Info
2014-11, ISPOR Europe 2014, Amsterdam, The Netherlands
Value in Health, Vol. 17, No. 7 (November 2014)
Code
HC1
Topic
Health Policy & Regulatory, Health Technology Assessment
Topic Subcategory
Decision & Deliberative Processes, Reimbursement & Access Policy
Disease
Multiple Diseases