Our real-world evidence found that a policy of KRAS testing with third-line cetuximab or panitumumab therapy was not cost effective at a threshold of CA$50,000/LYG. The change may be considered cost effective at higher threshold values, but there is considerable uncertainty around the cost-effectiveness estimate.

While many economic evaluations of cetuximab and panitumumab have been published, relatively few have evaluated the test–drug combination, and all rely on Markov models to estimate cost and survival outcomes. Previous cost-effectiveness analyses that included the cost of KRAS testing to identify cetuximab- or panitumumab-eligible patients report ICERs ranging from CA$43,000/QALY for cetuximab-irinotecan and CA$48,000/QALY for panitumumab [20] to approximately CA$180,000/QALY for single-agent cetuximab [23]. Our ICER results fall within this range, but the effectiveness estimated from the real-world data is less than half of the survival projections from decision models. In our analysis, we found incremental effectiveness to be 0.25 life-years or 0.22 QALYs. The incremental effectiveness values projected from decision models are 0.49–0.51 QALY [20, 21], using a lifetime time horizon. The real-world estimate may be lower for a number of reasons. First, our estimates use data for the full population of metastatic colorectal cancer cases, while the decision models rely on effectiveness estimates from clinical trial data. The real-world cohort includes patients who may not be considered eligible for clinical trials, including elderly patients, patients with more comorbidities or poor health status, and patients living in rural or remote areas [45]. Second, without a standard third-line therapy for patients ineligible for cetuximab or panitumumab, the study design relied on the end of second-line therapy to mark the start of observation. Many patients identified as potentially eligible for third-line systemic therapy using this definition would not realistically be candidates for therapy, due to deteriorating health status or death. A chart review from six Canadian cancer centres reported that only 43% of patients who received second-line therapy went on to receive third line [46]. The survival estimates presented here will likely be lower than for a study of prospectively identified candidates for third-line therapy, due to deaths shortly following the end of second-line therapy. Third, this study used an 8-year time horizon, rather than the lifetime projection in the decision models. The sensitivity analysis indicates that the ICER is sensitive to the time horizon, due to the differential impact on cost and effectiveness. Costs accrue early in the follow-up period, while survival benefits accrue later. Cost-effectiveness analyses with shorter time horizons have previously reported incremental 2.5-year survival of 0.18 LYG [23] and incremental 4-year survival of 0.29 LYG [47]. Based on the observed convergence of the survival curves, we expect that most benefits associated with the policy change have accrued by 8 years of follow-up, but the incremental survival may be underestimated.

We identified only one cost-effectiveness analysis of KRAS testing for third-line cetuximab or panitumumab that incorporated observational data. Uyl-de Groot et al. conducted a chart review of patients who received cetuximab or best supportive care (BSC) at eight hospitals in the Netherlands in 2009–2012, and used the data to build a Markov model to project long-term outcomes [47]. A challenge encountered by the authors, and a challenge common to the other model-based evaluations, was how to characterize the costs and survival of patients in the comparison arm, because there is no standard third-line therapy for patients who are ineligible for cetuximab and panitumumab. Even with detailed chart data, the authors were required to make assumptions about the progression-free survival in the BSC group to incorporate into their model. Furthermore, the costs of the BSC group were only one-tenth of costs for the cetuximab treatment group, suggesting that health services for the BSC group may not have been adequately captured by the hospital chart data. A strength of using administrative data to conduct this study is that it captures longitudinal health resource use across different services in the health care system. As a demonstration of the feasibility of combing real-world data with clinical trial data, the investigators of the CO.17 trial replicated their cost-effectiveness analysis using administrative data from Ontario, Canada [48]. They report ICERs very similar to the original trial results but conclude that the administrative data provide a far more complete assessment of benefits and cost, particularly for hospitalization and emergency department visits. The mean costs per patient in both treatment arms was roughly CA$12,000 to CA$15,000 higher using the administrative rather than the original trial data. In this analysis, we found that most of the incremental cost was made up of systemic therapy drug costs, but hospital costs, other outpatient prescription drugs, physician services, and other services contributed to the total. The use of real-world data provides a more comprehensive estimate of incremental cost and can directly capture cost and survival outcomes for patients receiving BSC, where there is no standard treatment protocol.

The results of sensitivity analysis scenarios show that the cost effectiveness of the policy of KRAS testing to inform cetuximab and panitumumab is not sensitive to the cost of the KRAS test. The ICER is most sensitive to changes in the costs of cetuximab or panitumumab, despite the fact that only one third of the post-policy cohort received either drug. There is the potential to considerably reduce the cost of anti-EGFR therapy with the introduction of biosimilars. The patents for both cetuximab and panitumumab have expired; there are currently no biosimilars on the market for either drug, but biosimilar cetuximab is reportedly in development [49]. A recent study re-analyzed data from the CO.17 trial to estimate the potential impact of biosimilar cetuximab on cost effectiveness [50]. The authors reported that at a price of CA$275.80 per 100 mg—a 15% reduction from the original study price of CA$324 per 100 mg—the ICER would be CA$261,126/QALY. In order to achieve a value of CA$100,000/QALY, the price would have to be lowered by over 80% [50]. In the current analysis, a 50% reduction in the cost of cetuximab and panitumumab resulted in an ICER near the threshold of CA$50,000/LYG. While a 50% reduction relative to the price of the branded biologic may not be attainable in practice, there is still significant opportunity to improve the value of therapy through the use of biosimilars [51]. In Canada, efforts are underway to coordinate the review and uptake of future biosimilars across provinces, through the Pan-Canadian Oncology Biosimilars Initiative.

This study was subject to several limitations, largely arising from the nature of the real-world data. The first challenge was assigning the eligibility date with administrative data. Administrative data are well suited to identifying services or encounters with the health care system, but the data do not capture all relevant clinical endpoints. Information such as progression of disease must be approximated using service-related definitions, such as the end of a course of chemotherapy [52]. In this analysis, we defined potential eligibility for third-line therapy using the end of second-line therapy, because it was the last reference date available for all patients in the population of interest. This definition of eligibility date has likely introduced some error in the analysis but is unlikely to bias the incremental cost-effectiveness analysis. The definition of eligibility is the same in both time periods, pre- and post-policy, and any error would occur equally in both groups.

Similarly, the lack of standard third-line therapy for patients ineligible for anti-EGFR therapy meant it was not feasible to identify an appropriate comparator for the subset of cetuximab- or panitumumab-treated patients. By using the end of second-line therapy to mark the start of observation, we included patients who went on to receive third-line cetuximab or panitumumab, or other chemotherapy for symptom management, and patients who died before they could initiate a new line of therapy. With the current study design, we are not able to estimate the cost effectiveness of cetuximab or panitumumab therapy without introducing immortal time bias. Patients who received cetuximab or panitumumab had to survive long enough to initiate at least one cycle of therapy by definition, while patients in the pre-policy period, or patients in the post-policy period ineligible for anti-EGFR therapy did not have an equivalent treatment start date available in the data.

This study roughly estimates QALYs using simple assumptions. Economic evaluation guidelines recommend the use of QALY in the reference case of the analysis, but there is little guidance for how to incorporate quality weights into cost-effectiveness analysis using observational data. There are initiatives underway in Canada to routinely collect more real-world quality-of-life data and other patient-reported outcomes, but little data are currently available [53, 54].

Lastly, there is a risk of bias from using a historical comparison group. Other changes in practice may have influenced patients’ survival or treatment costs. Over the study period, the uptake and duration of bevacizumab use for first-line therapy increased, colorectal cancer screening became more widespread, generic irinotecan became available in Canada, and two new regional cancer centers opened in BC. The impact of most of these changes would be seen earlier in the disease trajectory, before patients progressed to third-line therapy, but there could potentially be residual effects on total cost and overall survival. Our genetic matching approach can help to reduce the risk of bias from measured confounders, but there may be unmeasured confounders, including historical changes, that are unaccounted for in the study design.