Regression-based techniques for statistical decision making in single-case designs

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Nature of the Epidemic Threshold for the Susceptible-Infected-Susceptible Dynamics in Networks

We develop an analytical approach to the susceptible-infected-susceptible epidemic model that allows us to unravel the true origin of the absence of an epidemic threshold in heterogeneous networks. We find that a delicate balance between the number of high degree nodes in the network and the topological distance between them dictates the existence or absence of such a threshold. In particular, small-world random networks with a degree distribution decaying slower than an exponential have a vanishing epidemic threshold in the thermodynamic limit.

Effects of unit - based pricing on the waste collection demand : a meta - regression analysis

We perform a meta - analysis of 21 studies that estimate the elasticity of the price of waste collection demand upon waste quantities, a prior literature review having revealed that the price elasticity differs markedly. Based on a meta - regression with a total of 65 observations, we find no indication that municipal data give higher estimates for price elasticities than those associated with household data. Furthermore, there is no evidence that treating prices as exogenous underestimates the price elasticity. We find that much of the variation can be explained by sample size, the use of a weight - based as opposed to a volume - based pricing system, and the pricing of compostable waste. We also show that price elasticities determined in the USA and point estimations of elasticities are more elastic, but these effects are not robust to the changing of model specifications. Finally, our tests show that there is no evidence of publication bias while there is some evidence of the existence of genuine empirical effect.

Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

The risk factors for fractures and trabecular bone-score value in patients with endogenous Cushing's syndrome.

In a cohort study of 182 consecutive patients with active endogenous Cushing's syndrome, the only predictor of fracture occurrence after adjustment for age, gender bone mineral density (BMD) and trabecular bone score (TBS) was 24-h urinary free cortisol (24hUFC) levels with a threshold of 1472 nmol/24 h (odds ratio, 3.00 (95 % confidence interval (CI), 1.52-5.92); p = 0.002). INTRODUCTION: The aim was to estimate the risk factors for fracture in subjects with endogenous Cushing's syndrome (CS) and to evaluate the value of the TBS in these patients. METHODS: All enrolled patients with CS (n = 182) were interviewed in relation to low-traumatic fractures and underwent lateral X-ray imaging from T4 to L5. BMD measurements were performed using a DXA Prodigy device (GEHC Lunar, Madison, Wisconsin, USA). The TBS was derived retrospectively from existing BMD scans, blinded to clinical outcome, using TBS iNsight software v2.1 (Medimaps, Merignac, France). Urinary free cortisol (24hUFC) was measured by immunochemiluminescence assay (reference range, 60-413 nmol/24 h). RESULTS: Among enrolled patients with CS (149 females; 33 males; mean age, 37.8 years (95 % confidence interval, 34.2-39.1); 24hUFC, 2370 nmol/24 h (2087-2632), fractures were confirmed in 81 (44.5 %) patients, with 70 suffering from vertebral fractures, which were multiple in 53 cases; 24 patients reported non-vertebral fractures. The mean spine TBS was 1.207 (1.187-1.228), and TBS Z-score was -1.86 (-2.07 to -1.65); area under the curve (AUC) was used to predict fracture (mean spine TBS) = 0.548 (95 % CI, 0.454-0.641)). In the final regression model, the only predictor of fracture occurrence was 24hUFC levels (p = 0.001), with an increase of 1.041 (95 % CI, 1.019-1.063), calculated for every 100 nmol/24-h cortisol elevation (AUC (24hUFC) = 0.705 (95 % CI, 0.629-0.782)). CONCLUSIONS: Young patients with CS have a low TBS. However, the only predictor of low traumatic fracture is the severity of the disease itself, indicated by high 24hUFC levels.

The environmental effects of changing speed limits: a quantile regression approach

Two speed management policies were implemented in the metropolitan area of Barcelona aimed at reducing air pollution concentration levels. In 2008, the maximum speed limit was reduced to 80 km/h and, in 2009, a variable speed system was introduced on some metropolitan motorways. This paper evaluates whether such policies have been successful in promoting cleaner air, not only in terms of mean pollutant levels but also during high and low pollution episodes. We use a quantile regression approach for fixed effect panel data. We find that the variable speed system improves air quality with regard to the two pollutants considered here, being most effective when nitrogen oxide levels are not too low and when particulate matter concentrations are below extremely high levels. However, reducing the maximum speed limit from 120/100 km/h to 80 km/h has no effect – or even a slightly increasing effect –on the two pollutants, depending on the pollution scenario. Length: 32 pages

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

Threshold Selection Criteria for Quantification of Lumbosacral Cerebrospinal Fluid and Root Volumes from MRI

BACKGROUND AND PURPOSE: The high variability of CSF volumes partly explains the inconsistency of anesthetic effects, but may also be due to image analysis itself. In this study, criteria for threshold selection are anatomically defined. METHODS: T2 MR images (n = 7 cases) were analyzed using 3-dimentional software. Maximal-minimal thresholds were selected in standardized blocks of 50 slices of the dural sac ending caudally at the L5-S1 intervertebral space (caudal blocks) and middle L3 (rostral blocks). Maximal CSF thresholds: threshold value was increased until at least one voxel in a CSF area appeared unlabeled and decreased until that voxel was labeled again: this final threshold was selected. Minimal root thresholds: thresholds values that selected cauda equina root area but not adjacent gray voxels in the CSF-root interface were chosen. RESULTS: Significant differences were found between caudal and rostral thresholds. No significant differences were found between expert and nonexpert observers. Average max/min thresholds were around 1.30 but max/min CSF volumes were around 1.15. Great interindividual CSF volume variability was detected (max/min volumes 1.6-2.7). CONCLUSIONS: The estimation of a close range of CSF volumes which probably contains the real CSF volume value can be standardized and calculated prior to certain intrathecal procedures

The causal factors of international inequality in CO2 emissions per capita: a regression-based inequality decomposition analysis

This paper uses the possibilities provided by the regression-based inequality decomposition (Fields, 2003) to explore the contribution of different explanatory factors to international inequality in CO2 emissions per capita. In contrast to previous emissions inequality decompositions, which were based on identity relationships (Duro and Padilla, 2006), this methodology does not impose any a priori specific relationship. Thus, it allows an assessment of the contribution to inequality of different relevant variables. In short, the paper appraises the relative contributions of affluence, sectoral composition, demographic factors and climate. The analysis is applied to selected years of the period 1993–2007. The results show the important (though decreasing) share of the contribution of demographic factors, as well as a significant contribution of affluence and sectoral composition.

Optimization of wood flour acetylation by factorial design and partial least squares regression

Acetylation was performed to reduce the polarity of wood and increase its compatibility with polymer matrices for the production of composites. These reactions were performed first as a function of acetic acid and anhydride concentration in a mixture catalyzed by sulfuric acid. A concentration of 50%/50% (v/v) of acetic acid and anhydride was found to produced the highest conversion rate between the functional groups. After these reactions, the kinetics were investigated by varying times and temperatures using a 3² factorial design, and showed time was the most relevant parameter in determining the conversion of hydroxyl into carbonyl groups.