Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

Climate change caused by greenhouse gas emissions is of critical concern to international shipping. A large portfolio of mitigation measures has been developed to mitigate ship gas emissions by reducing ship energy consumption but is constrained by practical considerations, especially cost. There are difficulties in ranking the priority of mitigation measures, due to the uncertainty of ship information and data gathered from onboard instruments and other sources. In response, a neural network model is proposed to evaluate the cost-effectiveness of mitigation measures based on decarbonization. The neural network is further enhanced with a Bayesian method to consider the uncertainties of model parameters. Three of the key advantages of the proposed approach are (i) its ability to simultaneously consider a wide range of sources of information and data that can help improve the robustness of the modeling results; (ii) the ability to take into account the input uncertainties in ranking and selection; (iii) the ability to include marginal costs in evaluating the cost-effectiveness of mitigation measures to facilitate decision making. In brief, a negative “marginal cost-effectiveness” would indicate a priority consideration for a given mitigation measure. In the case study, it was found that weather routing and draft optimization could have negative marginal cost-effectiveness, signaling the importance of prioritizing these measures.

Cost-effectiveness of interferon alfa in chronic myelogenous leukemia.

PURPOSE To evaluate the cost-effectiveness of interferon alfa (IFN alpha) treatment of patients with chronic myelogenous leukemia relative to conventional chemotherapy. MATERIALS AND METHODS A decision-analysis model that involved a multistate Markov process was designed to estimate the expected cost and quality-adjusted life expectancies for two cohorts of patients to be administered conventional chemotherapy or IFN alpha. Two IFN alpha strategies were modeled: prolonged treatment for patients who achieved a hematologic response (scenario A) or only for patients who achieved a cytogenetic remission in a 2-year period (scenario B). Data on response and transition probabilities between health states were obtained from the literature by a MEDLINE search and pooled with a meta-analytic method. Costs were based on local charges. Expected survival was adjusted for quality of life on the basis of an expert panel judgment. RESULTS Baseline analysis showed IFN alpha treatment to increase the quality-adjusted life expectancy by 15.5 and 12.5 months relative to conventional chemotherapy, in scenarios A and B, respectively. Marginal cost-effectiveness was $89,500 and $63,500 per quality-adjusted life-year (QALY) gained. Sensitivity analysis confirmed IFN alpha as the most effective approach. Cost-effectiveness results were sensitive to the cost of IFN alpha therapy and to the assumptions about the rate of cytogenetic remission. Reducing the drug dose, as suggested by a recent report, would decrease the marginal cost-effectiveness to less than $20,000. CONCLUSION IFN alpha is substantially superior to conventional chemotherapy in terms of quality-adjusted survival, but, at the current doses, marginal cost-effectiveness ranges from $50,000 to $100,000 per QALY gained under most of our assumptions.