M-quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality

In this work, we intersect data on size-selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M-quantile regression model with Lasso and Elastic Net penalizations. This allows (i) to identify the best proxy for vehicular traffic via model selection, (ii) to investigate the relationship between fine PM concentration and the covariates at different M-quantiles of the conditional response distribution, and (iii) to be robust to the presence of outliers. Heterogeneity in the data is accounted by fitting a B-spline on the effect of the day of the year. Analytic and bootstrap-based variance estimates of the regression coefficients are provided, together with a numerical evaluation of the proposed estimation procedure. Empirical results show that atmospheric stability is responsible for the most significant effect on fine PM concentration: this effect changes at different levels of the conditional response distribution and is relatively weaker on the tails. On the other hand, model selection allows to identify the best proxy for vehicular traffic whose effect remains essentially the same at different levels of the conditional response distribution.

A spatial semiparametric M-quantile regression for hedonic price modelling

This paper proposes an M-quantile regression approach to address the heterogeneity of the housing market in a modern European city. We show how M-quantile modelling is a rich and flexible tool for empirical market price data analysis, allowing us to obtain a robust estimation of the hedonic price function whilst accounting for different sources of heterogeneity in market prices. The suggested methodology can generally be used to analyse nonlinear interactions between prices and predictors. In particular, we develop a spatial semiparametric M-quantile model to capture both the potential nonlinear effects of the cultural environment on pricing and spatial trends. In both cases, nonlinearity is introduced into the model using appropriate bases functions. We show how the implicit price associated with the variable that measures cultural amenities can be determined in this semiparametric framework. Our findings show that the effect of several housing attributes and urban amenities differs significantly across the response distribution, suggesting that buyers of lower-priced properties behave differently than buyers of higher-priced properties.

A nested error regression model with high-dimensional parameter for small area estimation

In this paper, we propose a flexible nested error regression small area model with high-dimensional parameter that incorporates heterogeneity in regression coefficients and variance components. We develop a new robust small area-specific estimating equations method that allows appropriate pooling of a large number of areas in estimating small area-specific model parameters. We propose a parametric bootstrap and jackknife method to estimate not only the mean squared errors but also other commonly used uncertainty measures such as standard errors and coefficients of variation. We conduct both model-based and design-based simulation experiments and real-life data analysis to evaluate the proposed methodology.

Studying the relationship between anxiety and school achievement: evidence from PISA data

Using the Programme for International Student Assessment (PISA) 2015 data for Italy, this paper offers a complete overview of the relationship between test anxiety and school performance by studying how anxiety affects the performance of students along the overall conditional distribution of mathematics, literature and science scores. We aim to indirectly measure whether higher goals increase test anxiety, starting from the hypothesis that high-skilled students generally set themselves high goals. We use an M-quantile regression approach that allows us to take into account the hierarchical structure and sampling weights of the PISA data. There is evidence of a negative and statistically significant relationship between test anxiety and school performance. The size of the estimated association is greater at the upper tail of the distribution of each score than at the lower tail. Therefore, our results suggest that high-performing students are more affected than low-performing students by emotional reactions to tests and school-work anxiety.

Efficacy and safety of robotic-assisted surgery in challenging hysterectomies - a single institutional experience

OBJECTIVE

An increasing number of robotic hysterectomies are being performed and the most common indication is fibroids. Fibroid uterus is common indication for hysterectomy for enlarged uteri. The role of robotic approach for complex pathologies as enlarged uterus is still debatable. The study aimed to analyze the feasibility of robotic hysterectomy in patients with enlarged uteri and the impact of uterine weight on surgical outcomes and on operative time length.

PATIENTS AND METHODS

One hundred and thirty-eight patients who underwent robotic hysterectomy for benign indications at the 2nd Division of Obstetrics and Gynecology, Azienda Ospedaliero-Universitaria Pisana, University of Pisa were consecutively enrolled.

RESULTS

Data of patients undergoing robotic surgery for benign indications were collected. Patients were stratified in two groups based on their uterine weight, to analyze the effective impact of uterine weight and dimension on surgical performance, operative time and postoperative outcomes. Conversion rate was 0%. Median uterine weight was 615 g (range 400-1900 g). Median total operating time was 131 minutes (range 70-255 minutes). Increase in uterine weight significantly increased operative times (p=0.003) and morcellation time (p=0.001). On the other hand, operative time was just partially influenced by route for removal of the uterus (p=0.085) but significantly affected by uterine weight (p=0.008), previous surgeries (p=0.003) and BMI of the patient (p=0.005).

CONCLUSIONS

Robotic hysterectomy is feasible and safe for challenging cases as large uteri. This technique could enable patients with outsized uteri, not suitable for vaginal hysterectomy, to undergo minimally invasive surgery with excellent results. Larger studies to investigate and compare robotic with other surgical approaches for difficult hysterectomies are needed to confirm these data.

Generalised M-quantile random-effects model for discrete response: An application to the number of visits to physicians

In this paper, we extend the linear M-quantile random intercept model (MQRE) to discrete data and use the proposed model to evaluate the effect of selected covariates on two count responses: the number of generic medical examinations and the number of specialised examinations for health districts in three regions of central Italy. The new approach represents an outlier-robust alternative to the generalised linear mixed model with Gaussian random effects and it allows estimating the effect of the covariates at various quantiles of the conditional distribution of the target variable. Results from a simulation experiment, as well as from real data, confirm that the method proposed here presents good robustness properties and can be in certain cases more efficient than other approaches.

Long-term outcome of inactive and active, low viraemic HBeAg-negative-hepatitis B virus infection: Benign course towards HBsAg clearance

BACKGROUND & AIMS

The difference between the long-term outcome of low-viraemic (HBV-DNA≤20 000-IU/mL, LV-AC) and inactive HBsAg carriers (HBV-DNA≤2000-IU/mL, IC) remains to be defined. We studied prospectively 153 HBeAg-negative HBsAg-carriers with baseline HBV-DNA≤20 000-IU/mL and normal transaminases.

METHODS

IC, LV-AC or chronic hepatitis B (CHB) (HBV-DNA persistently ≤2000-IU/mL, ≤20 000-IU/mL or >20 000-IU/mL respectively) were diagnosed after 1-year, 3-monthly monitoring. Thereafter IC and LV-AC were followed-up for additional 57.2 (8.5-158.3) months. HBV-DNA, HBsAg, HBV"core-related"Antigen (HBcrAg) and total-anti-HBc were quantified at baseline.

RESULTS

After the 1st year diagnostic follow-up CHB [higher HBV-DNA (P=.005), total-anti-HBc (P=.012), ALT (P=.007) and liver-stiffness (P=.021)] was identified in 20 (13.1%) carriers; baseline HBsAg≤1000IU/HBV-DNA≤2000IU/mL excluded the presence of CHB (NPV-100%). Thereafter, during the long-term follow-up none of 87 IC reactivated, 19 (21.8%) cleared HBsAg [older-age (P=.004), lower HBsAg (P<.001), higher yearly HBsAg decline (P<.001)]. Twenty-five of 46 (54.3%) LV-AC remained stable, 20 (43.5%) became IC and 1 (2.2%) developed CHB. The best single-point CHB and IC diagnostic-accuracies were total-anti-HBc (84.2%, NPV-98.2%) and HBV-DNA/total-anti-HBc/HBcrAg combination (89.5%, 93%-sensitivity, 84.8%-specificity) respectively.

CONCLUSIONS

Viraemia persistently ≤20 000-IU/mL predicts a benign clinical outcome: it was associated with transition to IC in 43% of LV-AC and to Occult HBV Infection in 20% of IC within 5-years. Nevertheless, 13.1% of individuals with low viraemia at presentation develops CHB within 1 year: 1-year HBV-DNA monitoring resulted the most accurate diagnostic approach that can be limited to at least a half of cases by the single point HBV-DNA/HBsAg quantification. The IC-diagnostic-accuracy combining HBV-DNA/total-anti-HBc/HBcrAg needs to be confirmed in further studies.

Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood.

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M-quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Disease mapping via negative binomial regression M-quantiles

We introduce a semi-parametric approach to ecological regression for disease mapping, based on modelling the regression M-quantiles of a negative binomial variable. The proposed method is robust to outliers in the model covariates, including those due to measurement error, and can account for both spatial heterogeneity and spatial clustering. A simulation experiment based on the well-known Scottish lip cancer data set is used to compare the M-quantile modelling approach with a disease mapping approach based on a random effects model. This suggests that the M-quantile approach leads to predicted relative risks with smaller root mean square error. The paper concludes with an illustrative application of the M-quantile approach, mapping low birth weight incidence data for English Local Authority Districts for the years 2005-2010.

Semiparametric M-quantile regression for count data

Lung cancer incidence over 2005–2010 for 326 Local Authority Districts in England is investigated by ecological regression. Motivated from mis-specification of a Negative Binomial additive model, a semiparametric Negative Binomial M-quantile regression model is introduced. The additive part relates to those univariate or bivariate smoothing components, which are included in the model to capture nonlinearities in the predictor or to account for spatial dependence. All such components are estimated using penalized splines. The results show the capability of the semiparametric Negative Binomial M-quantile regression model to handle data with a strong spatial structure.

Robust small area prediction for counts

A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative small area approaches.

Sleep disturbances in elderly subjects: an epidemiological survey in an Italian district

OBJECTIVES

Disturbed sleep is common in elderly people and has been related to comorbidities. The aim of this study was to evaluate the prevalence of sleep problems and their relationship with chronic disease in an elderly population.

MATERIALS AND METHODS

The whole population of subjects aged more than 65 years, in the municipality of Vecchiano, Pisa was considered as eligible and underwent a clinical interview and a questionnaire about insomnia, sleepiness, snoring and sleep apnea. A model of logistic regression was applied to the data.

RESULTS

The participation rate was 60.3% (1427 subjects). Insomnia was observed in 44.2% of our population, while sleepiness in 31.3%, snoring in 47.2% and sleep apnea in 9.0%. The most common diseases associated with sleep symptoms were depression, cognitive decline and diabetes.

CONCLUSIONS

Our results confirm that sleep problems are very common in elderly subjects and closely related to medical and psychiatric illnesses.