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Compared to conventional X-ray therapy, proton beam therapy (PBT) has more clinical and physical advantages such as irradiation dose reduction to normal tissues for pediatric medulloblastoma. However, PBT is expensive. We aimed to compare the cost-effectiveness of PBT for pediatric medulloblastoma with that of conventional X-ray therapy, while focusing on radiation-induced secondary cancers, which are rare, serious and negatively affect a patient’s quality of life (QOL). Based on a systematic review, a decision tree model was used for the cost-effectiveness analysis. This analysis was performed from the perspective of health care payers; the cost was estimated from medical fees. The target population was pediatric patients with medulloblastoma below 14 years old. The time horizon was set at 7.7 years after medulloblastoma treatment. The primary outcome was the incremental cost-effectiveness ratio (ICER), which was defined as the ratio of the difference in cost and lifetime attributable risk (LAR) between conventional X-ray therapy and PBT. The discount rate was set at 2% annually. Sensitivity analyses were performed to model uncertainty. Cost and LAR in conventional X-ray therapy and PBT were Japanese yen (JPY) 1 067 608 and JPY 2436061 and 42% and 7%, respectively. The ICER was JPY 3856398/LAR. In conclusion, PBT is more cost-effective than conventional X-ray therapy in reducing the risk of radiation-induced secondary cancers in pediatric medulloblastoma. Thus, our constructed ICER using LAR is one of the valid indicators for cost-effectiveness analysis in radiation-induced secondary cancer.

This research work focuses on integrating the multi attribute decision making with data mining in a fuzzy decision environment for customer relationship management. The main objective is to analyse the relation between multi attribute decision making and data mining considering a complex problem of ordering customers segments, which is based on four criteria of customer’s life time value, viz. length (L), recency (R), frequency (F) and monetary value (M). The proposed integrated approach involves fuzzy C-means (FCM) cluster analysis as data mining tool. The experiment conducted using MATLAB 12.0 for identifying eight clusters of customers. The two multi attribute decision making tools i.e., fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are used for ranking these identified clusters. The applicability of the integrated decision making technique is also demonstrated in this paper considering the case of Indian retail sector. This research collected responses from nine experts from Indian retail industry regarding their perception of relative importance of four criteria of customer life value and evaluated weights of each criterion using fuzzy AHP. Transaction data of 18 months of the case retail store was analysed to segment 1,600 customers into eight clusters using fuzzy c-means clustering analysis technique. Finally, these eight clusters were ranked using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The findings of this research could be helpful for firms in identifying the more valuable customers for them and allocate more resources to satisfy them. The findings will be also helpful in developing different loyalty program strategies for customers of different clusters.