Background: The study aimed to investigate the prognostic factors for patients with brain metastases undergoing radiosurgical treatment and to introduce a simple and practical scoring system for the prediction of survival time. Methods: We retrospectively analyzed data for 311 patients treated with Gamma Knife radiosurgery at a single institute. The mean age at time of treatment was 60 years (range 23-86 years), and the median Karnofsky performance status (KPS) score was 90 (range 60-100). Using a new prognostic index, the prognostic index for brain metastases (PIBM), the patients were categorized into 3 groups according to the primary tumor status and KPS score. We performed survival analysis and compared the prognostic ability of the PIBM with other published indices. Results: During the median follow-up duration of 8.2 months (range 0.1-109 months), the median overall survival time was 9.1 months. Stable primary tumor status (hazard ratio [HR] 0.497, 95% confidence interval [CI] 0.321-0.769, p = 0.002) and KPS score ≥90 (HR 1.407, 95% CI 1.018-1.946, p = 0.039) significantly predicted longer overall survival. The PIBM showed the lowest Akaike information criterion value and the highest integrated area under the curve value compared with other prognostic indices. Conclusions: The PIBM may be a more accurate prognostic indicator than other published indices. Although this new and practical prognostic index requires further validation in larger cohort studies, we suggest that the PIBM could be useful to predict survival time and inform appropriate management of patients with brain metastases.
The weather largely affects economic activity, and thus, companies vulnerable to weather risk need to plan ahead to cope with unexpected weather changes, just as they do for changes in interest rates, oil prices, or foreign exchange rates to stabilize their earning stream. Weather derivatives can be a useful tool for weather risk management. This paper focuses on pricing one of the most popular weather derivatives -HDD/CDD options- and estimating the market price of weather risk (MPR). Historical data are used to construct the stochastic process of temperature, while the current market prices of Chicago and New York HDD futures options are used to extract the implied MPR. The Monte-Carlo Simulation Method is proposed to estimate the price of weather derivatives numerically. In addition, the approximate closed form formula for the options is provided modifying the Alaton, Djehiche, and Stillberg (2002) model. Finally, option price sensitivity to changes in MPR is analyzed to show the important role of the MPR in the weather option pricing model.
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