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In this short communication piece, we draw attention to the discussion on policies for reducing energy poverty in European Union member states. We urgently need a policy approach able to support the transition from the current rising levels of energy poverty to a sustainable community with a greener and healthier future. Analysing energy prices, the policy framework and household income, we conduct a preliminary investigation of energy poverty from a macro-level perspective and associated policy interventions in the EU. Obtained from a non-classical fuzzy-set qualitative comparative analysis, our results clearly show that energy poverty is a product of substantial interdependence that may be summarised in two paths: (PP1) low or median household income and energy-policy focus, and (PP2) high energy prices and energy-policy focus. Surprisingly, our findings indicate that an energy-policy focus is found in EU member states with the highest levels of energy poverty. One step further, we also offer an insight into the absence of energy poverty, where social policy seems to play the key role. Taken together, we argue that member states facing above-average energy poverty are captured in an energy-poverty trap, whereby the existing energy-policy focus does not yield the desired results and the social policy is often too costly to implement due to the problem's magnitude. Our main concern is that prioritising any of the policies may slow down the transition to a sustainable energy society. We, therefore, call on scientists to not only further examine the energy-poverty phenomenon but to also participate in creating effective policies. • Energy-policy focus is present in EU states with the highest energy poverty. • Member states with low energy poverty have social- and energy-policy focus. • Policy efficacy depends on the scale of the problem and situation in the EU states. • Qualitative comparative analyses enrich investigation of energy poverty. [ABSTRACT FROM AUTHOR]

Objective: For rare events, one or two cells of 2x2 tables may have zero (0) counts or a count very close to zero. This issue makes the estimation of Odds Ratio (OR) impossible or leads to unstable OR estimates due to complete or quasi-complete separation. In such cases, Exact Logistic Regression and Firth approach may reduce the bias but these two methods do not resolve the issue completely. Material and Methods: In this research, we propose an Empirical Bayesian estimation procedure for Odds Ratio of rare events and provide a formal test procedure. We compare our Bayesian approach with Exact Logistic and Firth approaches in terms of statistical power achieved through extensive simulations using no event and varying event rate scenarios. Results: We show that our Bayesian approach retains the Type-I error rate and is much more powerful than the currently existing methods such as Exact Logistic Method and Firth approach. We also show that an Odds Ratio estimate is possible with our Bayesian approach even in no event or extremely rare events cases where the other two approaches suffer. Conclusion: Our new Bayesian estimation method is more powerful than Exact Logistic Regression and Firth approaches and provides an Odds ratio estimate even in no event or extremely rare events cases. [ABSTRACT FROM AUTHOR]

It is well known that non-small cell lung cancer (NSCLC) is a heterogeneous group of diseases. Previous studies have demonstrated genetic variation among different ethnic groups in the epidermal growth factor receptor (EGFR) in NSCLC. Research by our group and others has recently shown a lower frequency of EGFR mutations in African Americans with NSCLC, as compared to their White counterparts. In this study, we use our original study data of EGFR pathway genetics in African American NSCLC as an example to illustrate that univariate analyses based on aggregation versus partition of data leads to contradictory results, in order to emphasize the importance of controlling statistical confounding. We further investigate analytic approaches in logistic regression for data with separation, as is the case in our example data set, and apply appropriate methods to identify predictors of EGFR mutation. Our simulation shows that with separated or nearly separated data, penalized maximum likelihood (PML) produces estimates with smallest bias and approximately maintains the nominal value with statistical power equal to or better than that from maximum likelihood and exact conditional likelihood methods. Application of the PML method in our example data set shows that race and EGFR-FISH are independently significant predictors of EGFR mutation. [ABSTRACT FROM AUTHOR]

Exact inference for the logistic regression model is based on generating the permutation distribution of the sufficient statistics for the regression parameters of interest conditional on the sufficient statistics for the remaining (nuisance) parameters. Despite the availability of fast numerical algorithms for the exact computations, there are numerous instances where a data set is too large to be analyzed by the exact methods, yet too sparse or unbalanced for the maximum likelihood approach to be reliable. What is needed is a Monte Carlo alternative to the exact conditional approach which can bridge the gap between the exact and asymptotic methods of inference. The problem is technically hard because conventional Monte Carlo methods lead to massive rejection of samples that do not satisfy the linear integer constraints of the conditional distribution. We propose a network sampling approach to the Monte Carlo problem that eliminates rejection entirely. Its advantages over alternative saddlepoint and Markov Chain Monte Carlo approaches are also discussed. [ABSTRACT FROM AUTHOR]

Decamethylcyclopentasiloxane (D5) is a cyclic siloxane used in the production of industrial and consumer products. Four groups of 60 Fischer-344 female rats were analyzed for uterine endometrial adenocarcinoma (inhalation study with exposure levels in ppm/number of observed cases: 0/0, 10/1, 40/0, and 160/5) by exact regression (logistic, Poisson), the max poly-3 trend test, and a random effects probit model. When comparing the 160 ppm group to controls after 24 months, the incidence of adenocarcinomas was elevated (borderline significant); it was significant when all exposure levels were included. Four sets of (historical) control groups were formed, with varying heterogeneity. The effect of D5 was either significant or borderline significant when comparing all control sets to the 160 ppm group. When considering all exposure groups using any of the analysis methods, a significant effect was observed when the high dose group was included in the analysis; the effect was not significant when the high dose group was not included. The evidence tends to support the conclusion that D5 at the highest dose level (160 ppm) results in an increased incidence of adenocarcinomas. However, it is important to verify any potential effect through a biological investigation. [ABSTRACT FROM AUTHOR]