Cost-effectiveness analysis (CE Analysis) provides evidence about the incremental gains in patient outcomes costs from new treatments and interventions in cancer care. The utilization of “real-world” data allows these analyses to better reflect differences in costs and effects for actual patient populations with comorbidities and a range of ages as opposed to randomized controlled trials, which use a restricted population. This rapid review was done through PubMed and Google Scholar in July 2022. Relevant articles were summarized and data extracted to summarize changes in costs (in 2022 CAD) and effectiveness in cancer care once funded by the Canadian government payer system. We conducted statistical analyses to examine the differences between means and medians of costs, effects, and incremental cost effectiveness ratios (ICERs). Twenty-two studies were selected for review. Of those, the majority performed a CE Analysis on cancer drugs. Real-world cancer drug studies had significantly higher costs and effects than non-drug therapies. Studies that utilized a model to project longer time-horizons saw significantly smaller ICER values for the treatments they examined. Further, differences in drug costs increased over time. This review highlights the importance of performing real-world CE Analysis on cancer treatments to better understand their costs and impacts on a general patient population.

Cost-effectiveness analyses of new cancer treatments in real-world settings (e.g., post-clinical trials) inform healthcare decision makers about their healthcare investments for patient populations. The results of these analyses are often, though not always, presented with statistical uncertainty. This paper identifies five ways to characterize statistical uncertainty: (1) a 95% confidence interval (CI) for the incremental cost-effectiveness ratio (ICER); (2) a 95% CI for the incremental net benefit (INB); (3) an INB by willingness-to-pay (WTP) plot; (4) a cost-effectiveness acceptability curve (CEAC); and (5) a cost-effectiveness scatterplot. It also explores their usage in 22 articles previously identified by a rapid review of real-world cost effectiveness of novel cancer treatments. Seventy-seven percent of these articles presented uncertainty results. The majority those papers (59%) used administrative data to inform their analyses while the remaining were conducted using models. Cost-effectiveness scatterplots were the most commonly used method (34.3%), with 40% indicating high levels of statistical uncertainty, suggesting the possibility of a qualitatively different result from the estimate given. Understanding the necessity for and the meaning of uncertainty in real-world cost-effectiveness analysis will strengthen knowledge translation efforts to improve patient outcomes in an efficient manner.

Background: Smoking among patients diagnosed with cancer poses important health and financial challenges including reduced effectiveness of expensive cancer therapies. This study explores the value of smoking cessation programs (SCPs) for patients already diagnosed with cancer. It also identifies conditions under which SPCs may be wise investments. Methods: Using a simplified decision analytic model combined with insights from a literature review, we explored the cost-effectiveness of SCPs. Results: The findings provide insights about the potential impact of cessation probabilities among cancer patients in SCPs and the potential impact of SCPs on cancer patients’ lives. Conclusion: The evidence suggests that there is good reason to believe that SCPs are an economically attractive way to improve outcomes for cancer patients when SCPs are offered in conjunction with standard cancer care.