A Decision Analytical Model Investigating Cost-Effectiveness of Erlotinib

Background: A decision analytical model investigating cost-effectiveness of Erlotinib was submitted to the UK NICE (National Institute for Health and Care Excellence), which was not based on actual health-state transition probabilities, leading to structural uncertainty in the model. The study adopted a Markov state-transition model for investigating the cost-effectiveness of Erlotinib versus Best Supportive Care (BSC) as a maintenance therapy for patients with non-small cell lung cancer (NSCLC). Methods: Unlike manufacturer submission (MS), the Markov model was governed by transition probabilities, and allowed a negative post-progression survival (PPS) estimate to appear in later cycle. Using published summary survival data, the study employs three fixed- and time-varying approaches to estimate state transition probabilities that are used in a restructured model. Results: Post-progression probabilities and probabilities of death for Erlotinib were different than fixed-transition approaches. The best fitting curves are achieved for both PPS and probability of death across the time for which data were available, but the curves start diverging towards the end of this period. The Markov model which extrapolates the curves forward in time suggests that this difference between a time-varying and fixed-transition becomes even greater. Our models produce an ICER of 54k - 66k per QALY gain, which is comparable to an ICER presented in the MS (55k/QALY gain). Conclusions: Results from restructured Markov models show robust cost-effectiveness results for Erlotinib vs BSC. Although these are comparable to manufacturer submissions, in terms of magnitude they vary, and which are crucial for interventions falling near a threshold value. The study will further explore the cost-effectiveness of therapies for NSCLC in Qatar