Lead Researcher(s): Matthew Cheung
Lead Institution: Sunnybrook Research Institute
Co-Investigators: Kelvin Chan, Annette Hay, Nicole Mittmann, Natasha Leighl
ARCC Program Area(s): Health Technology Assessment
Funding Term: 2017
As the costs of cancer care continue to rise, clinical trials will increasingly need to demonstrate that new therapeutic options can offer both clinical benefit and value for money. Prospective cost-effectiveness analyses (CEAs) embedded within randomized trials allow for formal evaluation of value as a secondary outcome alongside conventional efficacy determinations. Ideally, sample size calculations for an economic appraisal would follow standard methods. However, hypothesis testing can be challenging given the uncertainty around future therapy costs, societal values (e.g. willingness to pay thresholds) and the lack of pre-existing estimates around cost and effectiveness (and their variances) a priori. As such, the optimal sample size (or available power based on a convenience sample) and data requirements to ensure adequate estimation of incremental cost-effectiveness remain unclear.
Since trials are limited by the costs associated with patient accrual and with the practical collection of the resource utilization data, it would be important to determine the optimal data collection and statistical analysis to ensure judicious trial performance. Re-analysis of previously-completed cost-effectiveness analyses that were embedded into Canadian Cancer Trial Group (CCTG) trials could clarify the optimal design elements and data requirements for future analyses. Analysis of the covariance correlation matrices within these studies could facilitate power calculations based on available sample sizes in future CEAs. Re-analysis of costs collected in previously-analysed trials to determine which high cost items have the largest impact on the economic sub-study results. Moreover, a resource’s influence on the overall costs might help determine which parameters should be collected within a trial, while at the same time identifying parameters that do not need to be collected. We hypothesize that the total cost of an intervention in a trial is often driven only by a few resource parameters; often drug or hospitalization costs, and that other resource parameters such as laboratory costs or diagnostic costs play a small role in the overall cost and therefore may be omitted from data collection.
(1) To determine the impact of reducing resource categories on the overall cost values and incremental cost-effectiveness ratios calculations within previously-completed CCTG trials.
(2) To determine whether variance/covariance of cost and effectiveness data (from previously-completed CCTG trials) can be used to generate power calculation tables for future CCTG clinical trials.