The South Korean government required the submission of economic evidence when it implemented the Positive-List System in December 2006. This study investigates the key factors that influenced actual public insurance reimbursement decisions, including the role of economic evidence, after 10 years of decision practice under compulsory health technology assessment (HTA) for new drugs. Logistic regression analysis was used to estimate the impact of the variables involved, including cost-effectiveness ratio as a key variable, on reimbursement decisions. The latter were defined as “yes” or “no” at a submitted price and indication. Only cases (n = 91) that present a cost-effectiveness ratio, and that have been reviewed based on this ratio from January 2007 to December 2016, were included in the analysis. Cases with higher cost-effectiveness ratios were less likely to be accepted. In addition, drugs that were used to treat severe diseases and drugs with no substitute were more likely to be recommended. The probability of acceptance declined along with the level of uncertainty in the submitted evidence. The acceptance rate for severe-disease drugs has increased since 2013, when the government introduced several policies that lowered the existing barriers to positive reimbursement. However, such an increase was not statistically significant. Cost-effectiveness is one of the most influential factors in drug-reimbursement decisions. However, inclusion of other explanatory variables, in addition to the cost-effectiveness ratio, predicted the results of decisions more accurately.

Purpose: We aimed to describe the types of uncertainties examined in the economic evaluations submitted for reimbursement in Korea and their impact on the incremental cost-effectiveness ratio (ICER).Method: Fifty dossiers were submitted by pharmaceutical companies to the economic subcommittee of the Pharmaceutical Benefit Coverage Advisory Committee (PBCAC) from January 2014 to December 2018. The types of uncertainties were categorized as structural and parametric, and the frequencies of the sensitivity analysis per variables were analyzed. The impact of uncertainties was measured by the percent variance of the ICER relative to that of the base case analysis.Results: Of the 50 submissions, varying discount rate (44 submissions), followed by time horizon (38 submissions) and model assumptions (29 submissions), were most frequently used to examine structural uncertainty, while utility (42 submissions), resource use (41 submissions), and relative effectiveness (26 submissions) were used to examine parametric uncertainty. A total of 1,236 scenarios (a scenario corresponds to a case where a single variable is varied by a single range) were presented in the one-way sensitivity analyses, where parametric and structural sensitivity analyses comprised 679 and 557 scenarios, respectively. Varying drug prices had the highest impact on ICER (median variance 19.9%), followed by discount rate (12.2%), model assumptions (11.9%), extrapolation (11.8%), and time horizon (10.0%).Conclusions: Variables related to long-term assumptions, such as model assumptions, time horizon, extrapolation, and discounting rate, were related to a high level of uncertainty. Caution should be exercised when using immature data.