Cost-Effectiveness of Treatment Sequences of Chemotherapies and Targeted Biologics for Elderly Metastatic Colorectal Cancer Patients

Quality of life and cost of strategies of two chemotherapy lines in metastatic colorectal cancer: results of the FFCD 2000-05 trial

Objectives: This study compared the cost and quality of life (QoL) of 407 advanced colorectal cancer patients, randomly assigned to receive LV5FU2 followed by FOLFOX6 (sequential strategy) or FOLFOX6 followed by FOLFIRI (combination strategy). Methods: Costs were compared from the French health insurance perspective, until the end of the second line of treatment. Consumed resources, collected during the trial, included medicines, hospitalizations, examinations, and transportation. Valuations were made using 2009 and 2016 tariffs. QoL was assessed using the QLQ-C30 questionnaire and clinically significant variations were searched. Results: In 2009, the mean cost per patient was significantly lower for the sequential strategy compared to the combination strategy (18,061€ and 23,119€, p = 0.001). In 2016, the difference was no longer significant (16,876€ and 18,090€, p = 0.41) because oxaliplatin and irinotecan became generics. The QoL analysis (292 patients) showed that there was significantly less improvement of global health status in the sequential strategy than in the combination strategy (29% and 42%; p = 0.02) during first-line therapy. No significant differences were observed for emotional functioning (p = 0.45) and physical functioning (p = 0.07) or during second-line therapy. Conclusion: The choice to treat patients with advanced colorectal cancer using one or the other strategy cannot be based on costs or QoL.

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models

Background

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes.

Methods

Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study.

Results

Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes.

Conclusions

Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12874-017-0437-y) contains supplementary material, which is available to authorized users.