Early-Phase Clinical Trials and Reimbursement Submissions to the Pan-Canadian Oncology Drug Review

Conditional Funding Recommendations for Drugs in Canada: A Cross-Sectional Analysis

BACKGROUND AND OBJECTIVE

Before listing by drug plans, new drugs that receive regulatory bodies' approval are assessed by the Health Technology Assessment agencies, including the Canadian Agency for Drugs and Technologies in Health (CADTH). Considering the impact of CADTH recommendations on payers' listing decisions, the growing number of high-cost drugs, and conditional listings, conditional recommendations have increasingly been of interest to the users of CADTH recommendations. This study aims to review the conditional reimbursement recommendations issued by CADTH and explores the reasons for those recommendations.

METHODS

All final drug reimbursement recommendations posted on the CADTH website since 1 January, 2003 were reviewed. Recommendations were included if accepted for review by 1 May, 2021 and completed before 1 January, 2022. The complete records excluding 'requests for advice' were categorized into 'reimburse,' 'do not reimburse,' and 'reimburse with criteria/condition(s)'. The identified variables were extracted and analyzed descriptively.

RESULTS

Of the 889 submissions, 785 (88.6%) completed recommendations were considered relevant to the study purpose. Of 750 recommendations, excluding 35 (4.5%) 'requests for advice,' 500 (66.6%) were 'reimburse with criteria/condition(s)'. 'Clinical criteria' at 481 (96.2%), 'cost-effectiveness improvement to an acceptable level' at 132 (26.4%), and 'the treatment cost not to exceed the cost of the alternative option(s)' at 118 (23.6%) were the most frequently reported conditions. The most frequent reasons for conditional reimbursement were 'clinical benefit and economic evaluation' at 304 (60.8%). Of conditional recommendations, 468 (93.6%) included more than one reason.

CONCLUSIONS

CADTH has facilitated patient access to innovative drugs even with limited evidence by conditional recommendations. The clinical criteria are the cornerstone of conditional recommendations. CADTH has developed the assessment process over time.

Impact of limited sample size and follow-up on single event survival extrapolation for health technology assessment: a simulation study

INTRODUCTION

Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up.

METHODS

Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30-500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error).

RESULTS

The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples.

CONCLUSIONS

Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model's extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.

Describing Sources of Uncertainty in Cancer Drug Formulary Priority Setting across Canada

Over the years, there have been significant advances in oncology. However, the rate that therapeutics come to market has increased, while the strength of evidence has decreased. Currently, there is limited understanding about how these uncertainties are managed in provincial funding decisions for cancer therapeutics. We conducted qualitative interviews with six senior officials from four different Canadian provinces (British Columbia, Alberta, Quebec, and Ontario) and a document review of the uncertainties found in submissions to the pan-Canadian Oncology Drug Review (pCODR). Participants reported considerable uncertainty related to a lack of solid clinical evidence (early-phase clinical trials: generalizability, immature data, and the use of unvalidated surrogate outcomes). Proposed strategies to deal with the uncertainty included risk-sharing agreements, collection of real-world evidence (RWE), and ongoing collaboration between federal groups and provinces. The document review added to the reported uncertainties by classifying them into five main categories: trial validity, population, comparators, outcomes, and intervention. This study highlights how decision makers must deal with significant amounts of uncertainty in funding decisions for cancer drugs, most of which stems from methodological limitations in clinical trials. There is a critical need for transparent priority-setting processes and mechanisms to reevaluate drugs to ensure benefit given the high level of uncertainty of novel therapeutics.