Qualités psychométriques de la version française de la TAS-20 et prévalence de l'alexithymie chez 264 adolescents tout-venant [The 20-item Toronto alexithymia scale: structural validity, internal consistency and prevalence of alexithymia in a Swiss adolescent sample]

L'objectif de cette étude est d'examiner la structure factorielle et la consistance interne de la TAS-20 sur un échantillon d'adolescents (n = 264), ainsi que de décrire la distribution des caractéristiques alexithymiques dans cet échantillon. La structure à trois facteurs de la TAS-20 a été confirmée par notre analyse factorielle confirmatoire. La consistance interne, mesurée à l'aide d'alpha de Cronbach, est acceptable pour le premier facteur (difficulté à identifier les sentiments (DIF)), bonne pour le second (difficulté à verbaliser les sentiments (DDF)), mais en revanche, faible pour le troisième facteur (pensées orientées vers l'extérieur (EOT)). Les résultats d'une Anova mettent en évidence une tendance linéaire indiquant que plus l'âge augmente plus le niveau d'alexithymie (score total TAS-20), la difficulté à identifier les sentiments et les pensées orientées vers l'extérieur diminuent. En ce qui concerne la prévalence de l'alexithymie, on remarque en effet que 38,5 % des adolescents de moins de 16 ans sont considérés comme alexithymiques, contre 30,1 % des 16-17 ans et 22 % des plus de 17 ans. Notre étude indique donc que la TAS-20 est un instrument adéquat pour évaluer l'alexithymie à l'adolescence, tout en suggérant quelques précautions étant donné l'aspect développemental de cette période.

Associations between alcohol consumption and selected cytokines in a Swiss population-based sample (CoLaus study).

To assess the associations between alcohol consumption and cytokine levels (interleukin-1beta - IL-1β; interleukin-6 - IL-6 and tumor necrosis factor-α - TNF-α) in a Caucasian population. Population sample of 2884 men and 3201 women aged 35-75. Alcohol consumption was categorized as nondrinkers, low (1-6 drinks/week), moderate (7-13/week) and high (14+/week). No difference in IL-1β levels was found between alcohol consumption categories. Low and moderate alcohol consumption led to lower IL-6 levels: median (interquartile range) 1.47 (0.70-3.51), 1.41 (0.70-3.32), 1.42 (0.66-3.19) and 1.70 (0.83-4.39) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p<0.01, but this association was no longer significant after multivariate adjustment. Compared to nondrinkers, moderate drinkers had the lowest odds (Odds ratio=0.86 (0.71-1.03)) of being in the highest quartile of IL-6, with a significant (p<0.05) quadratic trend. Low and moderate alcohol consumption led to lower TNF-α levels: 2.92 (1.79-4.63), 2.83 (1.84-4.48), 2.82 (1.76-4.34) and 3.15 (1.91-4.73) pg/ml for nondrinkers, low, moderate and high drinkers, respectively, p<0.02, and this difference remained borderline significant (p=0.06) after multivariate adjustment. Moderate drinkers had a lower odds (0.81 [0.68-0.98]) of being in the highest quartile of TNF-α. No specific alcoholic beverage (wine, beer or spirits) effect was found. Moderate alcohol consumption is associated with lower levels of IL-6 and (to a lesser degree) of TNF-α, irrespective of the type of alcohol consumed. No association was found between IL-1β levels and alcohol consumption.

Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

Accuracy of a new transmittance-reflectance pulse oximetry sensor in critically ill neonates.

OBJECTIVE: To test the accuracy of a new pulse oximeter sensor based on transmittance and reflectance. This sensor makes transillumination of tissue unnecessary and allows measurements on the hand, forearm, foot, and lower limb. DESIGN: Prospective, open, nonrandomized criterion standard study. SETTING: Neonatal intensive care unit, tertiary care center. PATIENTS: Sequential sample of 54 critically ill neonates (gestational age 27 to 42 wks; postnatal age 1 to 28 days) with arterial catheters in place. MEASUREMENTS AND MAIN RESULTS: A total of 99 comparisons between pulse oximetry and arterial saturation were obtained. Comparison of femoral or umbilical arterial blood with transcutaneous measurements on the lower limb (n = 66) demonstrated an excellent correlation (r2 = .96). The mean difference was +1.44% +/- 3.51 (SD) % (range -11% to +8%). Comparison of the transcutaneous values with the radial artery saturation from the corresponding upper limb (n = 33) revealed a correlation coefficient of 0.94 with a mean error of +0.66% +/- 3.34% (range -6% to +7%). The mean difference between noninvasive and invasive measurements was least with the test sensor on the hand, intermediate on the calf and arm, and greatest on the foot. The mean error and its standard deviation were slightly larger for arterial saturation values < 90% than for values > or = 90%. CONCLUSION: Accurate pulse oximetry saturation can be acquired from the hand, forearm, foot, and calf of critically ill newborns using this new sensor.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Comparing population distributions from bin-aggregated sample data: an application to historical height data from France

This paper develops a methodology to estimate the entire population distributions from bin-aggregated sample data. We do this through the estimation of the parameters of mixtures of distributions that allow for maximal parametric flexibility. The statistical approach we develop enables comparisons of the full distributions of height data from potential army conscripts across France's 88 departments for most of the nineteenth century. These comparisons are made by testing for differences-of-means stochastic dominance. Corrections for possible measurement errors are also devised by taking advantage of the richness of the data sets. Our methodology is of interest to researchers working on historical as well as contemporary bin-aggregated or histogram-type data, something that is still widely done since much of the information that is publicly available is in that form, often due to restrictions due to political sensitivity and/or confidentiality concerns.

System GMM estimation with a small sample

Properties of GMM estimators for panel data, which have become very popular in the empirical economic growth literature, are not well known when the number of individuals is small. This paper analyses through Monte Carlo simulations the properties of various GMM and other estimators when the number of individuals is the one typically available in country growth studies. It is found that, provided that some persistency is present in the series, the system GMM estimator has a lower bias and higher efficiency than all the other estimators analysed, including the standard first-differences GMM estimator.

On making causal claims: A review and recommendations

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity--which includes omitted variables, omitted selection, simultaneity, common methods bias, and measurement error--renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation is confounded, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66 % and up to 90 % of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

Knowledge of Catalan, public/private sector choice and earnings: Evidence from a double sample selection model

This paper explores the earnings return to Catalan knowledge for public and private workers in Catalonia. In doing so, we allow for a double simultaneous selection process. We consider, on the one hand, the non-random allocation of workers into one sector or another, and on the other, the potential self-selection into Catalan proficiency. In addition, when correcting the earnings equations, we take into account the correlation between the two selectivity rules. Our findings suggest that the apparent higher language return for public sector workers is entirely accounted for by selection effects, whereas knowledge of Catalan has a significant positive return in the private sector, which is somewhat higher when the selection processes are taken into account.

Externalizing disorders and substance use: empirically derived subtypes in a population-based sample of adults.

PURPOSE: Attention-deficit/hyperactivity disorder (ADHD), conduct disorder (CD), and oppositional defiant disorder (ODD) are common externalizing disorders of childhood. The common effects of these disorders on substance abuse need further investigation. The current study investigated the joint clusters of childhood/adolescence ADHD, CD, and ODD, and their influence on substance abuse/dependence in a population-based sample of adults. METHODS: The data were drawn from the PsyCoLaus study (n = 3,720) conducted in Lausanne, Switzerland. The population-based sample included 238 subjects meeting criteria for ADHD/ODD/CD diagnoses before the age of 15. Latent class analyses (LCA) were performed to derive comorbidity subtypes, which were subsequently characterized with respect to psychosocial correlates and substance use. RESULTS: The best fit in LCAs was achieved with three latent classes: an ADHD subtype (35.7 %); an externalizing multimorbid subtype (33.6 %) involving ODD, ADHD, and CD; and a third subtype with CD (30.7 %). The CD subtype showed the highest association with substance use. Apart from this, the externalizing multimorbid subtype was also significantly linked to substance use. The ADHD subtype had only elevated frequencies for alcohol dependence in comparison with subjects that had no history of ADHD, ODD, and CD during childhood or adolescence. Finally, important interactions between subtypes and sex were observed with regard to substance use. CONCLUSIONS: This study provides evidence showing that subtyping the externalizing disorders, ADHD, ODD and CD, along their comorbidity patterns leads to important differences regarding substance use. This could have implications for the etiology, prevention, and treatment of substance use disorders.

Consommation d'alcool à risque dans un collectif de patients de la Policlinique médicale universitaire de Lausanne [High risk alcohol consumption in a sample of patients of the Lausanne University Medical Polyclinic].

OBJECTIVES AND METHODS: This study indicates the prevalence, the characteristics, and the screening methods of patients with at risk alcohol drinking at the University Medical Clinic of Lausanne. RESULTS: The results reported demonstrate that one patient out of six is a drinker at risk without criteria for alcohol-dependance. The questionnaire AUDIT (Alcohol Use Disorders Identification Test) with a cut-off of five points seems to be the best screening test for at risk alcohol consumption. CONCLUSIONS: The high prevalence of at risk drinking in this study, combined with scientific evidence of the efficiency of brief interventions in changing drinking habits, emphasises the importance of alcohol screening for all patients attending outpatient medical settings.

Association between circulating cytokine levels, diabetes and insulin resistance in a population-based sample (CoLaus study).

OBJECTIVE: The associations between inflammation, diabetes and insulin resistance remain controversial. Hence, we assessed the associations between diabetes, insulin resistance (using HOMA-IR) and metabolic syndrome with the inflammatory markers high-sensitive C-reactive protein (hs-CRP), interleukin-1 beta (IL-1β), interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α). DESIGN: Cross-sectional study. PARTICIPANTS: Two thousand eight hundred and eighty-four men and 3201 women, aged 35-75, participated in this study. METHODS: C-reactive protein was assessed by immunoassay and cytokines by multiplexed flow cytometric assay. In a subgroup of 532 participants, an oral glucose tolerance test (OGTT) was performed to screen for impaired glucose tolerance (IGT). RESULTS: IL-6, TNF-α and hs-CRP were significantly and positively correlated with fasting plasma glucose (FPG), insulin and HOMA-IR. Participants with diabetes had higher IL-6, TNF-α and hs-CRP levels than participants without diabetes; this difference persisted for hs-CRP after multivariate adjustment. Participants with metabolic syndrome had increased IL-6, TNF-α and hs-CRP levels; these differences persisted after multivariate adjustment. Participants in the highest quartile of HOMA-IR had increased IL-6, TNF-α and hs-CRP levels; these differences persisted for TNF-α and hs-CRP after multivariate adjustment. No association was found between IL-1β levels and all diabetes and insulin resistance markers studied. Finally, participants with IGT had higher hs-CRP levels than participants with a normal OGTT, but this difference disappeared after controlling for body mass index (BMI). CONCLUSION: We found that subjects with diabetes, metabolic syndrome and increased insulin resistance had increased levels of IL6, TNF-α and hs-CRP, while no association was found with IL-1β. The increased inflammatory state of subjects with IGT is partially explained by increased BMI.

Nonclassical measurement error with applications to labor and development issues

Zero correlation between measurement error and model error has been assumed in existing panel data models dealing specifically with measurement error. We extend this literature and propose a simple model where one regressor is mismeasured, allowing the measurement error to correlate with model error. Zero correlation between measurement error and model error is a special case in our model where correlated measurement error equals zero. We ask two research questions. First, we wonder if the correlated measurement error can be identified in the context of panel data. Second, we wonder if classical instrumental variables in panel data need to be adjusted when correlation between measurement error and model error cannot be ignored. Under some regularity conditions the answer is yes to both questions. We then propose a two-step estimation corresponding to the two questions. The first step estimates correlated measurement error from a reverse regression; and the second step estimates usual coefficients of interest using adjusted instruments.