Oncology from an HTA and Health Economic Perspective

Evaluation of drug cost savings related to clinical trials from the perspective of a university hospital

OBJECTIVES

Clinical trials are an opportunity for patients to access innovative therapy, but patient inclusion in clinical trials can also result in cost savings for hospitals. Our objective was to evaluate the economic impact of clinical trials drug cost savings in a French academic institution from the perspectives of both the French Health Insurance (FHI) and hospitals.

METHODS

A retrospective, observational, cost saving analysis was performed on all the clinical trials initiated in our university hospital between 2015 and 2020. Only trials involving an investigational medicinal product were considered. Drug cost savings were defined as the best standard of care, defined in the protocol, whose cost was covered by a sponsor.

RESULTS

Of the 646 trials undertaken during the 6 years analysed, 21% (212/646) led to cost savings, mostly driven by the industrial sponsor (92%, €6 984 283/€7 591 612) for a total of €7 591 612 (91% from the FHI's perspective (€6 959 115/€7 591 612)). Oncology trials generated 79.1% (€6 004 966/€7 591 612) of global cost savings, mostly driven by onco-haematology (33.1%, €1 983 146/€6 004 966), onco-pneumology (29.2%, €1 754 333/€6 004 966) and onco-dermatology (23.5%, €1 409 553/€6 004 966) followed by hepatogastroenterology trials (6.9%, €413 113/€6 004 966). Of the 162 drugs, the top 15 generated 75.3% (€5 715 479/€7 591 612) of savings and were grouped together: 12 antineoplastic agents (six per os and six intravenous) and three per os antiviral for hepatitis C.

CONCLUSIONS

With ever-changing prices and new innovative treatments, such cost avoidance must be regularly evaluated. We provided objective evidence that clinical trials could achieve potential cost savings for the FHI and hospitals, in addition to the potential benefit to patients of having access to innovative investigational medicinal products.

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.

Considering potential solutions for limitations and challenges in the health economic evaluation of gene therapies

INTRODUCTION

The limited evidence in the clinical trials of gene therapies (GTs) posed substantial challenges for a reliable health technology assessment (HTA). This paper provides insights into the relationship between the background of diseases and the health economics assessment of GTs.Areas covered: The impacts of differentiated severity and unmet needs of genetic diseases, on the economic analysis of GTs, were discussed.Expert opinion: GTs offer a potential cure or significant clinical improvement, while limitations in clinical evidence constitute major obstacles for a robust assessment of clinical effectiveness and economic outcomes. This uncertainty may be balanced by the severity of the targeted condition and the associated unmet needs, thus leading to a relatively higher acceptance for GTs. Overtime, HTA agencies will become more demanding on comprehensive evidence of long-term effectiveness. With a growing number of GTs on the horizon, to what extent the unmet needs of previously devastating diseases will be fulfilled remain unclear. Nonetheless, comparative studies, either with a historical control group or existing treatments, will be necessary to demonstrate the additional benefits associated with GTs.