Insurance: an R-Program to Model Insurance Data

Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie?s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and [epsilon]-support vector regression from modern statistical machine learning. --Claim size,insurance tariff,logistic regression,statistical machine learning,support vector regression

Insurance: an R-Program to Model Insurance Data

Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie’s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and epsilon-support vector regression from modern statistical machine learning

Insurance: an R-Program to Model Insurance Data

Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie’s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and ε−support vector regression from modern statistical machine learning

Determination Of Hyper-Parameters For Kernel Based Classification And Regression

We investigate methods to determine appropriate choices of the hyper-parameters for kernel based methods. Support vector classification, kernel logistic regression and support vector regression are considered. Grid search, Nelder-Mead algorithm and pattern search algorithm are used

The effect of exacerbations on lung density in α1-antitrypsin deficiency

Background Acute exacerbations of COPD (AECOPD) have unclear impacts on emphysema measurement using computed tomography (CT)-derived 15th percentile lung density (PD15). The aim of this study was to assess the influence of AECOPD on PD15 lung density in α1-antitrypsin deficiency. Methods In a post hoc analysis of the RAPID (Randomised Trial of Augmentation Therapy in α1-Proteinase Inhibitor Deficiency) trial, raw marginal residuals of PD15 (measured − predicted) were determined by fitting a regression line to individual patient CT data. These deviations from the expected slope were compared by age, sex, baseline forced expiratory volume in 1 s, diffusing capacity of the lungs for carbon monoxide % predicted and PD15, inhaled corticosteroid use and treatment group. Results Positive and negative residuals (reflecting higher or lower lung density than predicted from regression) were observed, which declined in magnitude over time following AECOPD events. Logistic regression confirmed a limited effect of patient characteristics on the absolute size of residuals, whereas AECOPD within 6 weeks of CT had a notable effect versus no AECOPD within 6 weeks (OR 5.707, 95% CI 3.375–9.652; p<0.0001). Conclusion AECOPD result in higher or lower CT lung density estimates; the effect is greatest in the 2 weeks immediately after an AECOPD and persists for <6 weeks. Patient characteristics were less relevant than AECOPD within 6 weeks, supporting the reliability of PD15 as a measure of lung density. An exacerbation-free period prior to CT scan is advisable to reduce signal-to-noise ratio in future clinical trials

Effect of intravenous FX06 as an adjunct to primary percutaneous coronary intervention for acute ST-segment elevation myocardial infarction results of the F.I.R.E. (Efficacy of FX06 in the Prevention of Myocardial Reperfusion Injury) trial

OBJECTIVES: The purpose of this study was to investigate whether FX06 would limit infarct size when given as an adjunct to percutaneous coronary intervention. BACKGROUND: FX06, a naturally occurring peptide derived from human fibrin, has been shown to reduce myocardial infarct size in animal models by mitigating reperfusion injury. METHODS: In all, 234 patients presenting with acute ST-segment elevation myocardial infarction were randomized in 26 centers. FX06 or matching placebo was given as intravenous bolus at reperfusion. Infarct size was assessed 5 days after myocardial infarction by late gadolinium enhanced cardiac magnetic resonance imaging. Secondary outcomes included size of necrotic core zone and microvascular obstruction at 5 days, infarct size at 4 months, left ventricular function, troponin I levels, and safety. RESULTS: There were no baseline differences between groups. On day 5, there was no significant difference in total late gadolinium enhanced zone in the FX06 group compared with placebo (reduction by 21%; p = 0.207). The necrotic core zone, however, was significantly reduced by 58% (median 1.77 g [interquartile range 0.0, 9.09 g] vs. 4.20 g [interquartile range 0.3, 9.93 g]; p > 0.025). There were no significant differences in troponin I levels (at 48 h, -17% in the FX06 group). After 4 months, there were no longer significant differences in scar size. There were numerically fewer serious cardiac events in the FX06-treated group, and no differences in adverse events. CONCLUSIONS: In this proof-of-concept trial, FX06 reduced the necrotic core zone as one measure of infarct size on magnetic resonance imaging, while total late enhancement was not significantly different between groups. The drug appears safe and well tolerated. (Efficacy of FX06 in the Prevention of Myocardial Reperfusion Injury [F.I.R.E.]; NCT00326976)