Azacitidine for Treating Acute Myeloid Leukaemia with More Than 30 % Bone Marrow Blasts: An Evidence Review Group Perspective of a National Institute for Health and Care Excellence Single Technology Appraisal.

The National Institute for Health and Care Excellence (NICE) invited the manufacturer of azacitidine (Celgene) to submit evidence for the clinical and cost effectiveness of this drug for the treatment of acute myeloid leukaemia with more than 30 % bone marrow blasts in adults who are not eligible for haematopoietic stem cell transplantation, as part of the NICE's Single Technology Appraisal process. The Peninsula Technology Assessment Group was commissioned to act as the Evidence Review Group (ERG). The ERG produced a critical review of the evidence contained within the company's submission to NICE. The clinical effectiveness data used in the company's economic analysis were derived from a single randomised controlled trial, AZA-AML-001. It was an international, multicentre, controlled, phase III study with an open-label, parallel-group design conducted to determine the efficacy and safety of azacitidine against a conventional care regimen (CCR). The CCR was a composite comparator of acute myeloid leukaemia treatments currently available in the National Health Service: intensive chemotherapy followed by best supportive care (BSC) upon disease relapse or progression, non-intensive chemotherapy followed by BSC and BSC only. In AZA-AML-001, the primary endpoint was overall survival. Azacitidine appeared to be superior to the CCR, with median overall survival of 10.4 and 6.5 months, respectively. However, in the intention-to-treat analysis, the survival advantage associated with azacitidine was not statistically significant. The company submitted a de novo economic evaluation based on a partitioned survival model with four health states: "Remission", "Non-remission", "Relapse/Progressive disease" and "Death". The model time horizon was 10 years. The perspective was the National Health Service and Personal Social Services. Costs and health effects were discounted at the rate of 3.5 % per year. The base-case incremental cost-effectiveness ratio (ICER) of azacitidine compared with the CCR was £20,648 per quality-adjusted life-year (QALY) gained. In the probabilistic sensitivity analysis, the mean ICER was £17,423 per QALY. At the willingness-to-pay of £20,000, £30,000 and £50,000 per QALY, the probability of azacitidine being cost effective was 0.699, 0.908 and 0.996, respectively. The ERG identified a number of errors in Celgene's model and concluded that the results of the company's economic evaluation could not be considered robust. After amendments to Celgene's model, the base-case ICER was £273,308 per QALY gained. In the probabilistic sensitivity analysis, the mean ICER was £277,123 per QALY. At a willingness-to-pay of £100,000 per QALY, the probability of azacitidine being cost effective was less than 5 %. In all exploratory analyses conducted by the ERG, the ICER exceeded the NICE's cost-effectiveness threshold range of £20,000-30,000 per QALY. Given the evidence provided in the submission, azacitidine did not fulfil NICE's end-of-life criteria. After considering the analyses performed by the ERG and submissions from clinician and patient experts, the NICE Appraisal Committee did not recommend azacitidine for this indication.

Improved curve fits to summary survival data: application to economic evaluation of health technologies.

BACKGROUND: Mean costs and quality-adjusted-life-years are central to the cost-effectiveness of health technologies. They are often calculated from time to event curves such as for overall survival and progression-free survival. Ideally, estimates should be obtained from fitting an appropriate parametric model to individual patient data. However, such data are usually not available to independent researchers. Instead, it is common to fit curves to summary Kaplan-Meier graphs, either by regression or by least squares. Here, a more accurate method of fitting survival curves to summary survival data is described. METHODS: First, the underlying individual patient data are estimated from the numbers of patients at risk (or other published information) and from the Kaplan-Meier graph. The survival curve can then be fit by maximum likelihood estimation or other suitable approach applied to the estimated individual patient data. The accuracy of the proposed method was compared against that of the regression and least squares methods and the use of the actual individual patient data by simulating the survival of patients in many thousands of trials. The cost-effectiveness of sunitinib versus interferon-alpha for metastatic renal cell carcinoma, as recently calculated for NICE in the UK, is reassessed under several methods, including the proposed method. RESULTS: Simulation shows that the proposed method gives more accurate curve fits than the traditional methods under realistic scenarios. Furthermore, the proposed method achieves similar bias and mean square error when estimating the mean survival time to that achieved by analysis of the complete underlying individual patient data. The proposed method also naturally yields estimates of the uncertainty in curve fits, which are not available using the traditional methods. The cost-effectiveness of sunitinib versus interferon-alpha is substantially altered when the proposed method is used. CONCLUSIONS: The method is recommended for cost-effectiveness analysis when only summary survival data are available. An easy-to-use Excel spreadsheet to implement the method is provided.