Herramientas estadísticas para el estudio de perfiles de riesgo

En este documento se ilustra de un modo práctico, el empleo de tres instrumentos que permiten al actuario definir grupos arancelarios y estimar premios de riesgo en el proceso que tasa la clase para el seguro de no vida. El primero es el análisis de segmentación (CHAID y XAID) usado en primer lugar en 1997 por UNESPA en su cartera común de coches. El segundo es un proceso de selección gradual con el modelo de regresión a base de distancia. Y el tercero es un proceso con el modelo conocido y generalizado de regresión linear, que representa la técnica más moderna en la bibliografía actuarial. De estos últimos, si combinamos funciones de eslabón diferentes y distribuciones de error, podemos obtener el aditivo clásico y modelos multiplicativos

Association between smoking and total energy expenditure in a multi-country study.

BACKGROUND: The association between smoking and total energy expenditure (TEE) is still controversial. We examined this association in a multi-country study where TEE was measured in a subset of participants by the doubly labeled water (DLW) method, the gold standard for this measurement. METHODS: This study includes 236 participants from five different African origin populations who underwent DLW measurements and had complete data on the main covariates of interest. Self-reported smoking status was categorized as either light (<7 cig/day) or high (≥7 cig/day). Lean body mass was assessed by deuterium dilution and physical activity (PA) by accelerometry. RESULTS: The prevalence of smoking was 55% in men and 16% in women with a median of 6.5 cigarettes/day. There was a trend toward lower BMI in smokers than non-smokers (not statistically significant). TEE was strongly correlated with fat-free mass (men: 0.70; women: 0.79) and with body weight (0.59 in both sexes). Using linear regression and adjusting for body weight, study site, age, PA, alcohol intake and occupation, TEE was larger in high smokers than in never smokers among men (difference of 298 kcal/day, p = 0.045) but not among women (162 kcal/day, p = 0.170). The association became slightly weaker in men (254 kcal/day, p = 0.058) and disappeared in women (-76 kcal/day, p = 0.380) when adjusting for fat-free mass instead of body weight. CONCLUSION: There was an association between smoking and TEE among men. However, the lack of an association among women, which may be partly related to the small number of smoking women, also suggests a role of unaccounted confounding factors.

Herramientas estadísticas para el estudio de perfiles de riesgo

En este documento se ilustra de un modo práctico, el empleo de tres instrumentos que permiten al actuario definir grupos arancelarios y estimar premios de riesgo en el proceso que tasa la clase para el seguro de no vida. El primero es el análisis de segmentación (CHAID y XAID) usado en primer lugar en 1997 por UNESPA en su cartera común de coches. El segundo es un proceso de selección gradual con el modelo de regresión a base de distancia. Y el tercero es un proceso con el modelo conocido y generalizado de regresión linear, que representa la técnica más moderna en la bibliografía actuarial. De estos últimos, si combinamos funciones de eslabón diferentes y distribuciones de error, podemos obtener el aditivo clásico y modelos multiplicativos

Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA)

Background: MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample.Results: Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace.Conclusion: Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

Treatment of ruptured abdominal aortic aneurysm, a permanent challenge or a waste of resources? Prediction of outcome using a multi-organ-dysfunction score.

OBJECTIVES: in a retrospective study, attempts have been made to identify individual organ-dysfunction risk profiles influencing the outcome after surgery for ruptured abdominal aortic aneurysms. METHODS: out of 235 patients undergoing graft replacement for abdominal aortic aneurysms, 57 (53 men, four women, mean age 72 years [s.d. 8.8]) were treated for ruptured aneurysms in a 3-year period. Forty-eight preoperative, 13 intraoperative and 34 postoperative variables were evaluated statistically. A simple multi-organ dysfunction (MOD) score was adopted. RESULTS: the perioperative mortality was 32%. Three patients died intraoperatively, four within 48 h and 11 died later. A significant influence for pre-existing risk factors was identified only for cardiovascular diseases. Multiple linear-regression analysis indicated that a haemoglobin <90 g/l, systolic blood pressure <80 mmHg and ECG signs of ischaemia at admission were highly significant risk factors. The cause of death for patients, who died more than 48 h postoperatively, was mainly MOD. All patients with a MOD score >/=4 died (n=7). These patients required 27% of the intensive-care unit (ICU) days of all patients and 72% of the ICU days of the non-survivors. CONCLUSION: patients with ruptured aortic aneurysms from treatment should not be excluded. However, a physiological scoring system after 48 h appears justifiable in order to decide on the appropriateness of continual ICU support.

Genome-wide profiling of blood pressure in adults and children.

Hypertension is an important determinant of cardiovascular morbidity and mortality and has a substantial heritability, which is likely of polygenic origin. The aim of this study was to assess to what extent multiple common genetic variants contribute to blood pressure regulation in both adults and children and to assess overlap in variants between different age groups, using genome-wide profiling. Single nucleotide polymorphism sets were defined based on a meta-analysis of genome-wide association studies on systolic blood pressure and diastolic blood pressure performed by the Cohort for Heart and Aging Research in Genome Epidemiology (n=29 136), using different P value thresholds for selecting single nucleotide polymorphisms. Subsequently, genetic risk scores for systolic blood pressure and diastolic blood pressure were calculated in an independent adult population (n=2072) and a child population (n=1034). The explained variance of the genetic risk scores was evaluated using linear regression models, including sex, age, and body mass index. Genetic risk scores, including also many nongenome-wide significant single nucleotide polymorphisms, explained more of the variance than scores based only on very significant single nucleotide polymorphisms in adults and children. Genetic risk scores significantly explained ≤1.2% (P=9.6*10(-8)) of the variance in adult systolic blood pressure and 0.8% (P=0.004) in children. For diastolic blood pressure, the variance explained was similar in adults and children (1.7% [P=8.9*10(-10)] and 1.4% [P=3.3*10(-5)], respectively). These findings suggest the presence of many genetic loci with small effects on blood pressure regulation both in adults and children, indicating also a (partly) common polygenic regulation of blood pressure throughout different periods of life.

Fecundity versus offspring size in the greater white-toothed shrew Crocidura russula

1. The relationships between female body mass (WWal)i, tter size (m), juvenile growth rate (G) and mass at weaning (W20) were examined by monitoring natural litters in 29 greater white-toothed shrews, Crocidura russula (Hermann 1780). The trade-offs between m and G or W20 were further investigated by manipulating litter sizes: each of seven females reared four litters of 2, 4, 6 and 8 offspring. 2. Offspring mass at weaning (W20) exhibited a large variance, most of which could be attributed (ANCOVA on manipulated litters) to two effects: a litter-size effect, and a female individual effect, referred to as 'female quality'. 3. Litter size explained 68% of the variance in W20 among manipulated litters (linear regression). The limited milk supply was probably responsible for this effect, because litter size depressed growth rate during the first half of the lactation period (G1), but not during the weaning stage (G2). 4. Among non-manipulated litters, litter size correlated positively with maternal body mass (Wa), so that large females tended to produce small juveniles. This correlation between m and Wa is seen as the result of a body-mass dependence in the cost of raising a litter of a given size, during either pregnancy or lactation. 5. Differences in 'female quality' explained 16% of the variance in W20 among manipulated litters. This factor did not affect GI and may thus relate to differences among offspring of different females in their rates of processing milk and/or external food during late lactation. 6. 'Female quality' was independent of both body mass and litter size: larger females did not produce larger offspring when controlled for litter size, while higher-quality females did not produce larger litters. 7. Our results support the hypothesis that most variance in adult and juvenile body masses is non-genetic, and stems from the trade-off between litter size and offspring size.

Ecophysiological response of Sorghum halepense populations to reduced rates of nicosulfuron

The control and regrowth after nicosulfuron reduced rate treatment of Johnsongrass (Sorghum halepense L. Pers.) populations, from seven Argentinean locations, were evaluated in pot experiments to assess if differential performance could limit the design and implementation of integrated weed management programs. Populations from humid regions registered a higher sensibility to reduced rates of nicosulfuron than populations from subhumid regions. This effect was visualised in the values of regression coefficient of the non-linear models (relating fresh weight to nicosulfuron rate), and in the time needed to obtain a 50% reduction of photosynthesis rate and stomatal conductance. The least leaf CO2 exchange of subhumid populations could result in a lower foliar absorption and translocation of nicosulfuron, thus producing less control and increasing their ability to sprout and produce new aerial biomass. The three populations from subhumid regions, with less sensibility to nicosulfuron rates, presented substantial difference in fresh weight, total rhizome length and number of rhizome nodes, when they were evaluated 20 week after treatment. In consequence, a substantial Johnsongrass re-infestation could occur, if rates below one-half of nicosulfuron labeled rate were used to control Johnsongrass in subhumid regions.

SAT Modulo Linear Arithmetic for Solving Polynomial

Polynomial constraint solving plays a prominent role in several areas of hardware and software analysis and verification, e.g., termination proving, program invariant generation and hybrid system verification, to name a few. In this paper we propose a new method for solving non-linear constraints based on encoding the problem into an SMT problem considering only linear arithmetic. Unlike other existing methods, our method focuses on proving satisfiability of the constraints rather than on proving unsatisfiability, which is more relevant in several applications as we illustrate with several examples. Nevertheless, we also present new techniques based on the analysis of unsatisfiable cores that allow one to efficiently prove unsatisfiability too for a broad class of problems. The power of our approach is demonstrated by means of extensive experiments comparing our prototype with state-of-the-art tools on benchmarks taken both from the academic and the industrial world.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Social and medical vulnerability factors of frequent users at the emergency department: a case-control study

BACKGROUND: Up to 5% of patients presenting to the emergency department (ED) four or more times within a 12 month period represent 21% of total ED visits. In this study we sought to characterize social and medical vulnerability factors of ED frequent users (FUs) and to explore if these factors hold simultaneously. METHODS: We performed a case-control study at Lausanne University Hospital, Switzerland. Patients over 18 years presenting to the ED at least once within the study period (April 2008 toMarch 2009) were included. FUs were defined as patients with four or more ED visits within the previous 12 months. Outcome data were extracted from medical records of the first ED attendance within the study period. Outcomes included basic demographics and social variables, ED admission diagnosis, somatic and psychiatric days hospitalized over 12 months, and having a primary care physician.We calculated the percentage of FUs and non-FUs having at least one social and one medical vulnerability factor. The four chosen social factors included: unemployed and/or dependence on government welfare, institutionalized and/or without fixed residence, either separated, divorced or widowed, and under guardianship. The fourmedical vulnerability factors were: ≥6 somatic days hospitalized, ≥1 psychiatric days hospitalized, ≥5 clinical departments used (all three factors measured over 12 months), and ED admission diagnosis of alcohol and/or drug abuse. Univariate and multivariate logistical regression analyses allowed comparison of two JGIM ABSTRACTS S391 random samples of 354 FUs and 354 non-FUs (statistical power 0.9, alpha 0.05 for all outcomes except gender, country of birth, and insurance type). RESULTS: FUs accounted for 7.7% of ED patients and 24.9% of ED visits. Univariate logistic regression showed that FUs were older (mean age 49.8 vs. 45.2 yrs, p=0.003),more often separated and/or divorced (17.5%vs. 13.9%, p=0.029) or widowed (13.8% vs. 8.8%, p=0.029), and either unemployed or dependent on government welfare (31.3% vs. 13.3%, p<0.001), compared to non-FUs. FUs cumulated more days hospitalized over 12 months (mean number of somatic days per patient 1.0 vs. 0.3, p<0.001; mean number of psychiatric days per patient 0.12 vs. 0.03, p<0.001). The two groups were similar regarding gender distribution (females 51.7% vs. 48.3%). The multivariate linear regression model was based on the six most significant factors identified by univariate analysis The model showed that FUs had more social problems, as they were more likely to be institutionalized or not have a fixed residence (OR 4.62; 95% CI, 1.65 to 12.93), and to be unemployed or dependent on government welfare (OR 2.03; 95% CI, 1.31 to 3.14) compared to non-FUs. FUs were more likely to need medical care, as indicated by involvement of≥5 clinical departments over 12 months (OR 6.2; 95%CI, 3.74 to 10.15), having an ED admission diagnosis of substance abuse (OR 3.23; 95% CI, 1.23 to 8.46) and having a primary care physician (OR 1.70;95%CI, 1.13 to 2.56); however, they were less likely to present with an admission diagnosis of injury (OR 0.64; 95% CI, 0.40 to 1.00) compared to non-FUs. FUs were more likely to combine at least one social with one medical vulnerability factor (38.4% vs. 12.1%, OR 7.74; 95% CI 5.03 to 11.93). CONCLUSIONS: FUs were more likely than non-FUs to have social and medical vulnerability factors and to have multiple factors in combination.

Study of the impact of tissue density heterogeneities on 3-dimensional abdominal dosimetry: comparison between dose kernel convolution and direct monte carlo methods.

Dose kernel convolution (DK) methods have been proposed to speed up absorbed dose calculations in molecular radionuclide therapy. Our aim was to evaluate the impact of tissue density heterogeneities (TDH) on dosimetry when using a DK method and to propose a simple density-correction method. METHODS: This study has been conducted on 3 clinical cases: case 1, non-Hodgkin lymphoma treated with (131)I-tositumomab; case 2, a neuroendocrine tumor treatment simulated with (177)Lu-peptides; and case 3, hepatocellular carcinoma treated with (90)Y-microspheres. Absorbed dose calculations were performed using a direct Monte Carlo approach accounting for TDH (3D-RD), and a DK approach (VoxelDose, or VD). For each individual voxel, the VD absorbed dose, D(VD), calculated assuming uniform density, was corrected for density, giving D(VDd). The average 3D-RD absorbed dose values, D(3DRD), were compared with D(VD) and D(VDd), using the relative difference Δ(VD/3DRD). At the voxel level, density-binned Δ(VD/3DRD) and Δ(VDd/3DRD) were plotted against ρ and fitted with a linear regression. RESULTS: The D(VD) calculations showed a good agreement with D(3DRD). Δ(VD/3DRD) was less than 3.5%, except for the tumor of case 1 (5.9%) and the renal cortex of case 2 (5.6%). At the voxel level, the Δ(VD/3DRD) range was 0%-14% for cases 1 and 2, and -3% to 7% for case 3. All 3 cases showed a linear relationship between voxel bin-averaged Δ(VD/3DRD) and density, ρ: case 1 (Δ = -0.56ρ + 0.62, R(2) = 0.93), case 2 (Δ = -0.91ρ + 0.96, R(2) = 0.99), and case 3 (Δ = -0.69ρ + 0.72, R(2) = 0.91). The density correction improved the agreement of the DK method with the Monte Carlo approach (Δ(VDd/3DRD) < 1.1%), but with a lesser extent for the tumor of case 1 (3.1%). At the voxel level, the Δ(VDd/3DRD) range decreased for the 3 clinical cases (case 1, -1% to 4%; case 2, -0.5% to 1.5%, and -1.5% to 2%). No more linear regression existed for cases 2 and 3, contrary to case 1 (Δ = 0.41ρ - 0.38, R(2) = 0.88) although the slope in case 1 was less pronounced. CONCLUSION: This study shows a small influence of TDH in the abdominal region for 3 representative clinical cases. A simple density-correction method was proposed and improved the comparison in the absorbed dose calculations when using our voxel S value implementation.

TBS (Trabecular Bone Score) is More Sensitive Than BMD to Diabetes-Related Fracture Risk

Background:Type 2 diabetes (T2D) is associated with increased fracture risk but paradoxically greater BMD. TBS (trabecular bone score), a novel grey-level texture measurement extracted from DXA images, correlates with 3D parameters of bone micro-architecture. We evaluated the ability of lumbar spine (LS) TBS to account for the increased fracture risk in diabetes. Methods:29,407 women ≥50 years at the time of baseline hip and spine DXA were identified from a database containing all clinical BMD results for the Province of Manitoba, Canada. 2,356 of the women satisfied a well-validated definition for diabetes, the vast majority of whom (>90%) would have T2D. LS L14 TBS was derived for each spine DXA examination blinded to clinical parameters and outcomes. Health service records were assessed for incident non-traumatic major osteoporotic fracture codes (mean follow-up 4.7 years). Results:In linear regression adjusted for FRAX risk factors (age,BMI, glucocorticoids, prior major fracture, rheumatoid arthritis, COPD as a smoking proxy, alcohol abuse) and osteoporosis therapy, diabetes was associated with higher BMD for LS, femoral neck and total hip but lower LS TBS (all p<0.001). Similar results were seen after excluding obese subjects withBMI>30. In logistic regression (Figure), the adjusted odds ratio (OR) for a skeletal measurement in the lowest vs highest tertile was less than 1 for all BMD measurements but increased for LS TBS (adjusted OR 2.61, 95%CI 2.30-2.97). Major osteoporotic fractures were identified in 175 (7.4%) with and 1,493 (5.5%) without diabetes (p < 0.001). LS TBS predicted fractures in those with diabetes (adjusted HR 1.27, 95%CI 1.10-1.46) and without diabetes (HR 1.31, 95%CI 1.24-1.38). LS TBS was an independent predictor of fracture (p<0.05) when further adjusted for BMD (LS, femoral neck or total hip). The explanatory effect of diabetes in the fracture prediction model was greatly reduced when LS TBS was added to the model (indicating that TBS captured a large portion of the diabetes-associated risk), but was paradoxically increased from adding any of the BMD measurements. Conclusions:Lumbar spine TBS is sensitive to skeletal deterioration in postmenopausal women with diabetes, whereas BMD is paradoxically greater. LS TBS predicts osteoporotic fractures in those with diabetes, and captures a large portion of the diabetes-associated fracture risk. Combining LS TBS with BMD incrementally improves fracture prediction.