DISCUSSION

In cancer care, as in many other fields, medical decisions are often confronted with situations in which innovative treatments, which are evaluated through randomized clinical trial, fail to demonstrate a significant advantage in terms of overall survival but may offer other benefits for patients by reducing or delaying the risk of relapse. When this additional benefit is obtained while minimizing costs, adoption of the new treatment should be straightforward. However, in most cases, as in the case of HDC in our study, therapeutic innovations are more costly than standard treatment and devoting additional resources to obtain a limited benefit in quality of life, which does not translate into increased life expectancy, raises complex social issues. In such cases, handling of uncertainty in economic analysis becomes especially important to guarantee the robustness of the estimations and minimize the risk of devoting too many resources for a limited improvement.

In this study, we present the results of the CEA of the PEGASE 01 protocol designed to evaluate the benefit of adding a single course of CMA to a conventional 4 FEC100 chemotherapy regimen for node-positive patients with breast cancer. Our uncertainty analysis was threefold. First, we considered in the analysis two different clinical end points: disease-free survival and overall survival. The use of two different possible end points is a way to evaluate to which extent the choice of the clinical end point can reverse the results of CEA when definite conclusions are not evident. Second, we handled for sampling uncertainty in the cost minimization analysis (using bootstrap tests for cost comparisons) and in the CEA with the building of confidence interval around the ICER obtained. Third, as there are many other potential sources of uncertainty in economic analysis, which derive from the assumptions that need to be made, a sensitivity analysis on the key modeling parameters was performed.

Cost data were based on a detailed observation of medical records, because the goal was to calculate the real cost of the procedures. They were available from the entry in the study until 6 months after the end of treatment for 300 patients included in the protocol. Our cost assessment showed an additional cost of 12,650€, HDC being 3.4 times more expensive than standard-dose chemotherapy. These cost estimates were robust to various sensitivity analyses carried out on the key components of the total cost (changes in the unit price of the three major cost factors, decrease in the number of hospitalization stays). This cost assessment appeared consistent with those recently published on HDC and peripheral blood stem cell support (2;11;27). In particular, De Rosa et al. (11) evaluated the cost of HDC with blood stem cell support for breast cancer patients at 20,816€, the larger part of this cost being due to hospitalizations during the posttransplant phase.

Survival times were assessed directly from the clinical trial and complete clinical results have been described elsewhere (unpublished data, 2004). Survival results did not show any benefit in terms of OS, whereas DFS was significantly increased for patients intensified. These results confirmed those obtained by Rodenhuis et al.(30), who reported a DFS advantage relative to HDC for very poor prognosis patients.

Although all health economists agree on the necessity of accounting for the uncertain nature of economic evaluation through appropriate statistical tests and the use of sensitivity analyses (17;22), two authors have noted recently that, in practice, very few published studies used methods to examine uncertainty (1;6). Uncertainty due to sampling fluctuations can be handled through the building of confidence regions for the ICER and various methods for calculating such confidence regions have been explored in the literature such as Taylor's method (25;33), parametric and nonparametric bootstrap methods (7), and Fieller's method (10;38). Recently, a study (32) has shown that the truncated Fieller's method is quite perfect and stable in all situations, even with skewed data and in the case of the pair (mean costs difference, mean effects difference) approaching zero. This method, therefore, was used here to calculate ICER confidence regions. However, several types of uncertainty are likely to be present in any economic analysis and the ICER confidence interval is a way of addressing a particular form of uncertainty, the one deriving from sampling variation. Consequently, it is not a total substitute for any other way of trying to estimate uncertainties, in particular sensitivity analyses that are required in any CEA.

The first end point considered in our CEA was DFS, as this was the main end point for the clinical evaluation of this protocol. The ICER was evaluated to approximately 13,074€ per disease-free life years gained.

We have shown that the upper bound of the 95 percent CI was equal to around 38,000€ per disease-free life years gained; that value is around three times more than the point estimate of the ICER. Consequently, we have to be cautious about the findings of this analysis, which are no longer obvious and depends on the maximum acceptable value (or ceiling ratio) that society is willing to pay. If society is willing to pay at least 38,000€ to gain 1 disease-free life year, then intensive treatment is a cost-effective strategy with a 5 percent error rate. Otherwise, questions have to be asked, and the choice will depend on the preferences and priorities of society, including preferences of patients.

Generally, cost-effectiveness analyses present results in terms of cost per life year saved, using the usual criterion of OS. In our analysis, no difference in OS was found between treatments, but the significant cost difference (extra cost = 7,403€) indicated that HDC is a dominated strategy and should not be retained on the basis of this criterion. This finding is in contradiction with the conclusions we obtained with DFS as effectiveness criterion (of course, in this case, cost of relapse was not included in the total cost). This illustrates the very general difficulty of evaluating treatments that slightly improve survival while significantly increasing DFS, what commonly occurs in the field of cancer. In practice, this situation arises in many empirical studies, as clinical trials are often designed to detect small differences between treatments and medical innovations often involve a deterioration of the cost-effectiveness ratio to obtain a limited improvement in efficacy when compared with standard treatments. The question of the choice of the efficacy criterion therefore arises, as it is crucial for decision making. Is the usually used criterion of 5-year OS a satisfactory and definitive criterion, because DFS at 5 years is significantly different? A 10-year OS may be a more appropriate criterion, especially in the context of therapeutic strategies where treatments of relapse are able to maintain patients alive. However, in this case, the criterion is problematic, as we cannot wait for 10 years to make a clinical and economic evaluation of an innovation: the use of an intermediate criterion, therefore, clearly is required. These questions certainly will be the ones addressed in the future, as medical evaluations are more and more confronted with situations where there is not survival benefit. Moreover, even if the DFS is not a definite efficacy end point, it seems to be particularly relevant in the context of treatments where quality of life (QOL) is now largely recognized as an important element in decision making. Facing the choice of the appropriate effectiveness criteria, QOL becomes an important argument as several studies (8;18;34) have proved that the recurrence disease is associated with a poorer QOL. The recurrent phase of cancer has been shown to affect a very large number of QOL dimensions: physical, functional, emotional, pain, distress, and in particular, psychological sequels are a major effect related to recurrence (9;21). In that context, a gain in DFS is clearly related to a benefit for patients, at least in terms of QOL, and the criterion of DFS, therefore, is of great relevance. So, even if no difference in OS is observed, questions about the role of HDC have to be addressed. This question can be considered as a problem of trade-off between economic considerations and a gain in DFS, that is, a gain in welfare. Is society ready to pay for a treatment that would certainly contribute to patients' well-being by reducing the risk of relapse? However, even if DFS is a relevant criterion, choosing this end point means considering implicitly that living after relapse has no value. This hypothesis being quite important, we recalculated the ICER after weighting the time spent after relapse, thus doing an implicit hypothesis on QOL. We noticed that, calculating the ICER with a weight of 0.9 (which means that the time spent after relapse is equivalent to 90 percent of the time spent without) yields a practically identical value (41,052€/QALYs) to that of the upper bound of the confidence interval calculated for handling uncertainty. Conversely, the ICER with a weight of 0.1 for time after relapse led to a close value (9,760€/QALYs) to the lower bound of the confidence interval. Thus, handling uncertainty for decision making with calculation of a confidence interval for the ICER can be related to an implicit choice on the value of life after relapse, that is, implicit QOL hypotheses that differ completely, depending on which bound we are locating.

Lastly, with regard to the generalization of our results, we must remember one major problem often associated with economic evaluations carried out alongside the diffusion process of innovations. In all evaluation of an innovative treatment, a change in medical practice between the trial study and the today's practice often occurs. Substantial cost savings may be achieved in the present case by changing practice patterns to reduce hospital stays of patients receiving high-dose treatments. Even larger cost savings are now conceivable with the development of outpatient treatment modalities, largely tested in various trials (24;26). Some substantial cost savings will presumably occur for the use of intensive chemotherapy in today's practice compared with the trial practice. In addition, care in clinical trials often requires more resources than routine clinical practice. In particular, the total cost of intensification was clearly overestimated in our study. These arguments, therefore, weigh in favor of intensification, if this strategy is adopted in routine practice as a standard treatment.