Conditional approval of cancer drugs in Canada: accountability and impact on public funding

Evaluation of changes in the clinical benefits of oncology drugs over time following reimbursement using the ASCO-VF and the ESMO-MCBS

PURPOSE

This study aims to estimate changes in the value of oncology drugs over time from initial data of the reimbursement decisions to subsequent publications in Korea, using two value frameworks.

METHODS

We retrieved primary publications assessed for reimbursement between 2007 and July 2021 from the decision documents of Health Insurance Review and Assessment and subsequent publications made available following reimbursement decision from ClinicalTrials.Gov and PubMed databases. Changes in the clinical benefit scores were assessed using the American Society of Clinical Oncology Value Framework (ASCO-VF) and the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). A paired t test was performed to test whether there was a difference in the scores between primary and subsequent publications.

RESULTS

Of 73 anticancer product/indication pairs, 45 (61.6%) had subsequent publications, of which 62.5% were released within 1 year of reimbursement decision. The mean ESMO-MCBS and ASCO-VF Net Health Benefit scores increased from primary to subsequent publications, although the differences were not significant. The mean ASCO-VF bonus score significantly increased from 15.91 to 19.09 (p = 0.05). The ESMO-MCBS and bonus scores increased by 0.25 and 0.21, respectively, and the bonus score had a greater impact on the ESMO-MCBS score than the preliminary score did.

CONCLUSION

The value of drugs demonstrated in subsequent publications varies considerably among oncology drugs, depending on uncertainty associated with the initial evidence and the availability of updated evidence. As decision-making in the face of uncertainty becomes more prevalent, the value frameworks can serve as simple screening tools for re-evaluation in these cases.

How proven is a 'proven intervention'? Ethics of placebo controls in light of conditional approval programs

This article discusses the difficulties of establishing whether there exists a proven therapeutic intervention when regenerative experimental treatments are made accessible to patients under conditional approval programs (outside clinical trials). Conditional approvals are often made on the basis of less robust efficacy evidence than otherwise required for the registration of new treatments. Lower quality of evidence affects the ethical justification of using a placebo-control design. The absence of a proven intervention is important in evaluating whether it is ethically justifiable to use such a design in a clinical trial and is present in major ethical guidelines. The main argument in this paper is that conditionally approved therapies, if referred to as 'proven interventions', would make placebo-control design ethically unjustifiable. Conducting rigorous clinical trials after conditional approvals is crucial to establish the efficacy of therapeutic approaches under such approvals. Hindrances to running such trials and generating further efficacy evidence are brought to attention.

Impact of Regulatory Approval Status on CADTH Reimbursement of Oncology Drugs and Role of Real-World Evidence on Conditional Approvals from 2019 to 2021

Real-world evidence (RWE) is health and outcomes data generated from a patient's journey through the health care system or disease process (i.e., real-world data). RWE is now having an increasingly important role in regulatory/reimbursement decisions. This article examines reimbursement recommendations by the Canadian Agency for Drugs and Technology in Health (CADTH) on oncology drugs approved between 2019 and 2021. Oncology drugs with a Summary Basis of Decision (SBD) for original marketing approvals were used to generate a corresponding list of CADTH final clinical recommendations for review. Of the 45 oncology drugs approved by Health Canada, CADTH granted positive funding recommendations to all 11 drugs that had priority review approvals. Two of the 17 drugs with standard reviews did not file to CADTH and 3 received a negative recommendation. Of the 17 drugs with Notice of Compliance with Conditions (NOCc) status, three were not filed to CADTH and four were under active reviews. Of the ten completed NOCc reviews, all contained RWE from sponsors and six received a negative decision on their first review. No significant differences in review times were found between the three approval statuses. Regulatory approval status appeared to influence reimbursement outcomes in Canada and evaluation of 10 NOCc approvals provided little insight regarding robustness of RWE required for more favorable considerations.

FDA Accelerated Approval for Malignant Hematology and Oncology Indications in the Canadian Environment

Accelerated approval (AA) by the FDA enables earlier access to promising new therapies. Health Canada has a similar process. Canada implemented a national health technology assessment (HTA) for reimbursement decisions in 2011. This study evaluated regulatory and funding timelines and decisions for FDA AA cancer therapies in Canada. The FDA’s AA of malignant hematology and oncology from January 2000–December 2019 was reviewed. Dates from Health Canada, HTA decisions and provincial listings were collected. There were 94 FDA AAs, two of which were subsequently withdrawn. Of the 92 AAs, 70 received full (46)/conditional (24) Health Canada approval, and 22 were not filed. Since the introduction of HTA, 31 out of 45 of Health Canada’s approved indications underwent HTA review: 18 received a positive recommendation conditional on cost-effectiveness, 8 were not recommended and 5 were withdrawn/suspended. The median time from the AA to any Health Canada approval is 9.4 months, from any Health Canada approval to HTA decision is 5.8 months and from HTA decision to the first formulary listing is 12.0 months. The access and timeline for the first formulary listing differences were observed between the USA and Canada due to the decision of pharmaceutical companies to submit (or not) to regulatory/HTA bodies, national procedural delays with different healthcare delivery models and submission timelines. This study demonstrates that there is delayed access to promising new therapies in Canada.

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.

The future of research in hematology: Integration of conventional studies with real-world data and artificial intelligence

Most national health-care systems approve new drugs based on data of safety and efficacy from large randomized clinical trials (RCTs). Strict selection biases and study-entry criteria of subjects included in RCTs often do not reflect those of the population where a therapy is intended to be used. Compliance to treatment in RCTs also differs considerably from real world settings and the relatively small size of most RCTs make them unlikely to detect rare but important safety signals. These and other considerations may explain the gap between evidence generated in RCTs and translating conclusions to health-care policies in the real world. Real-world evidence (RWE) derived from real-world data (RWD) is receiving increasing attention from scientists, clinicians, and health-care policy decision-makers - especially when it is processed by artificial intelligence (AI). We describe the potential of using RWD and AI in Hematology to support research and health-care decisions.

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.

Measuring Quality of Life in Ovarian Cancer Clinical Trials-Can We Improve Objectivity and Cross Trial Comparisons?

Epithelial ovarian cancer (EOC) remains a lethal disease for the majority of women diagnosed with it worldwide. For the majority of patients, diagnosis occurs late, in the advanced setting. Disease-induced as well as treatment-related adverse events can negatively impact quality of life (QoL). Research to date has captured these data through use of patient-related outcomes (PROs) and, increasingly, has become an area of increased attention and focus in clinical trial reporting. QoL/PRO measurements in EOC clinical trials at different transition points in a patient's journey are increasingly being recognized by patients, clinicians and regulatory agencies as the key determinants of treatment benefit. Various context-specific PROs and PRO endpoints have been described for clinical trials in EOC. Standardized approaches and checklists for incorporating PRO endpoints in clinical trials have been proposed. In a real-world clinical practice setting, PRO/QoL measures, which are meaningful, valid, reliable, feasible and acceptable to patients and clinicians, need to be implemented and used. These may assist by serving as screening tools; helping with the identification of patient preferences to aid in decision making; improving patient-provider communication; facilitating shared decision making. Importantly, they may also improve quality of care through an increasingly patient-centered approach. Potential areas of future research include assessment of anxiety, depression and other mental health issues. In good prognostic groups, such as maintenance clinical trials, following patients beyond progression will capture possible downstream effects related to delaying the psychological trauma of relapse, symptoms due to disease progression and side-effects of subsequent chemotherapy. Identifying PRO endpoints in next-generation-targeted therapies (including immunotherapies) also warrants investigation.

Health-related quality of life in oncology drug reimbursement submissions in Canada: A review of submissions to the pan-Canadian Oncology Drug Review

BACKGROUND

In Canada, the Canadian Agency for Drugs and Technologies in Health (CADTH) evaluates and makes recommendations for the reimbursement of cancer drugs. One component of its recommendation is based on an economic evaluation, which typically takes the form of a cost-utility analysis. A cost-utility analysis measures the effects of competing therapies with quality-adjusted life-years (QALYs). The data for this calculation typically come from generic, preference-based measures of health-related quality of life (HRQOL). The objective of this review is to determine the frequency at which HRQOL data are collected alongside cancer drug trials and used in the cost-utility analysis submitted to the CADTH pan-Canadian Oncology Drug Review (pCODR).

METHODS

Submissions between 2015 and 2018 to pCODR, the group charged with evaluating cancer drug submissions at CADTH, were reviewed. All pCODR submissions, either in progress or completed, were publicly available online. The search was restricted to completed evaluations.

RESULTS

Forty-three submissions met the inclusion criteria. The incremental gain in QALYs in most submissions from the new technology was small (median incremental gain, 0.86; interquartile range, 0.6-1.39). More than half of the submissions (56%) did not include original data on HRQOL, with most relying on previous studies of variable relevance and quality. Re-analyses by pCODR based on concerns over HRQOL data used in the submitted model were common (52%).

CONCLUSIONS

Drug manufacturers do not consistently collect data on HRQOL alongside clinical trials and instead rely on evidence generated in previous studies to inform cost-utility analyses. These findings should induce manufacturers to collect original HRQOL data that are simultaneously relevant to patients and decision makers.