OBJECTIVES: A multi-dimensional approach to evidence synthesis has previously been explored as an alternative to analysis of hazard ratios (HR) frequently used for time-to-event data. The objective of this study was to explore and compare results using a more complex approach to network meta-analysis (NMA) with those using conventional methods. The methods were applied to an evidence base previously identified to evaluate comparative efficacy for the treatment of advanced breast cancer in postmenopausal women following failure of prior endocrine therapy. METHODS: Data were identified from a published systematic literature review previously conducted in this population; these data were synthesised within a Bayesian NMA utilising fractional polynomial models to estimate relative efficacy between different interventions for overall survival. KaplaneMeier curves reported by all studies were digitised to recreate virtual individual patient data. Extrapolated long-term survival profiles, expected (mean) survival and ranking probabilities were also estimated for each intervention. RESULTS: Seven trials were identified, which assesed seven comparators of interest. Fulvestrant 500 mg was found to be the most effective intervention; mean survival was over 3 years and there was around a 40% chance of fulvestrant 500 mg being ranked as the best treatment over a 10-year time horizon. Findings and trends were consistent with previously published NMAs utilising conventional methodology, synthesising HRs. CONCLUSIONS: A comprehensive approach to evidence synthesis may yield more informative, robust and reliable estimates of comparative efficacy than traditional NMA methods analysing trial-level HRs. These efficacy estimates may subsequently be incorporated into cost-effectiveness models without the assumption of proportional hazards between treatment arms that underpins the use of HRs, which have been shown to be a limited measure of treatment effect. An alternative approach to synthesis, utilising fractional polynomials models, may lead to more informed decision-making, which is critical when there are budget constraints associated with reimbursement of new interventions.
Background: A systematic review was conducted to understand clinical, economic and health-related quality-of-life outcomes in second-line biliary tract cancer. Materials & methods: The review followed established recommendations. The feasibility of network meta-analysis revealed limited networks, thus synthesis was limited to a summary of reported ranges, percentiles and medians. Results: The review included 62 trials and observational studies highly variable with respect to key baseline characteristics. Commonly evaluated second-line treatments included fluoropyrimidine-, gemcitabine- and S-1-based regimens. Across active treatment arms, median overall survival ranged from 3.5 to 15.0 months (median: 6.9), median progression-free survival from 1.4 to 6.5 months (median: 2.9) and objective response from 0 to 36.4%. Outcomes were similar between study types, with a few notable outliers. Treatment-related/-emergent adverse events were infrequently reported; no studies reported economic or health-related quality-of-life outcomes. Conclusions: Biliary tract cancer is a difficult-to-treat disease with poor prognosis. Despite evolving treatment landscapes, more recent studies did not show clinical outcome improvement, highlighting an unmet need among advanced/metastatic patients.
PFS (e.g. lognormal, Weibull) and a correlation parameter of the bivariate Normal distribution. Extrapolation functions can be different for PFS and OS. The uncertainty around the parameter estimates is assessed by using asymptotic properties of Maximum Likelihood estimates. We compute the total uncertainty around the parameter estimates by means of the Frobenius norm of the variance-covariance matrices. RESULTS: The total uncertainty associated with the independent modelling of OS and PFS is 0.0064, whereas the uncertainty associated with the joint modelling is 0.0057, approximately 10% less than the standard independent modelling approach. The joint approach can be easily implemented in Excel-based models. CONCLUSIONS: By using joint modelling we prevent the parametric extrapolations of PFS and OS to cross when running the probabilistic sensitivity analysis. Further, by accounting for the correlation between the two outcomes the total uncertainty around the estimates is reduced. Finally, this approach can be extended to other outcomes like treatment duration and PFS.
the ASSURE project. In this preliminary analysis, the comparative cost-effectiveness of personalised screening strategies and current practice was calculated as a cost-per-case-detected from a health service perspective. Uncertainty in the cost-effectiveness estimate is investigated using one-way sensitivity analyses of key parameters. Results: The incremental cost-effectiveness ratio of a three risk group stratification procedure in the base case was £45,617 per-case-detected. Influential parameters were sensitivity of mammography, recall rate, cancer growth parameters and accuracy of risk estimation. ConClusions: A very simple stratification procedure may not be cost-effective. The optimal risk stratification for personalised breast screening will be investigated to determine whether this offers improvement in cost-effectiveness.
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