Etti Doveh

Firefighters, critical incidents, and drinking to cope: the adequacy of unit-level performance resources as a source of vulnerability and protection.

The authors investigated the moderating role of unit-level performance resources on the distress-mediated relationship between the intensity of involvement in workplace critical incidents and problematic drinking behavior (i.e., drinking to cope). Building on recent developments in hierarchical linear modeling, the authors tested a cross-level, moderated-mediation model using data from 1,481 firefighters in 144 companies. The findings indicate that (a) there is a significant, distress-mediated association between intensity of involvement in such incidents and drinking to cope, which varies by company (i.e., unit), and (b) the adequacy of unit-level performance resources explains much of this cross-unit variance and attenuates both individual-level mediation stages (i.e., intensity of involvement in critical incidents 3 distress, and distress 3 drinking to cope). Implications regarding the role of unit resources adequacy as a vulnerability factor in stressor-strain relations are discussed.

Detection of Subclinical Keratoconus Using an Automated Decision Tree Classification

PURPOSE To develop a method for automatizing the detection of subclinical keratoconus based on a tree classification. DESIGN Retrospective case-control study. METHODS setting: University Hospital of Bordeaux. participants: A total of 372 eyes of 197 patients were enrolled: 177 normal eyes of 95 subjects, 47 eyes of 47 patients with forme fruste keratoconus, and 148 eyes of 102 patients with keratoconus. observation procedure: All eyes were imaged with a dual Scheimpflug analyzer. Fifty-five parameters derived from anterior and posterior corneal measurements were analyzed for each eye and a machine learning algorithm, the classification and regression tree, was used to classify the eyes into the 3 above-mentioned conditions. main outcome measures: The performance of the machine learning algorithm for classifying eye conditions was evaluated, and the curvature, elevation, pachymetric, and wavefront parameters were analyzed in each group and compared. RESULTS The discriminating rules generated with the automated decision tree classifier allowed for discrimination between normal and keratoconus with 100% sensitivity and 99.5% specificity, and between normal and forme fruste keratoconus with 93.6% sensitivity and 97.2% specificity. The algorithm selected as the most discriminant variables parameters related to posterior surface asymmetry and thickness spatial distribution. CONCLUSION The machine learning classifier showed very good performance for discriminating between normal corneas and forme fruste keratoconus and provided a tool that is closer to an automated medical reasoning. This might help in the surgical decision before refractive surgery by providing a good sensitivity in detecting ectasia-susceptible corneas.

Testing agreement for multi-item scales with the indicesrWG(J) and AD M(J)

The most popular index of agreement has been rWG(J); more recently, the ADM(J) index also has been used. This study addresses two problems: first, how to test the statistical significance of rWG(J) and ADM(J) and, second, how to infer from the indices that were evaluated for each group about the agreement of the ensemble of groups. The authors extend the inference based on either rWG(J) or ADM(J) by focusing on multiple-item scales and on the whole ensemble of groups. Their method is based on simulations, as was done by Dunlap, Burke, and Smith-Crowe (2003) and by Cohen, Doveh, and Eick (2001). The tests are illustrated on the data of Bliese, Halverson, and Schriesheim (2002) pertaining to a sample of 2,042 U.S Army soldiers in 49 U.S. Army companies. Software for our procedures is available both as a SAS code and in the Multilevel Modeling in R package (Bliese, 2006).

Further Insight and Additional Inference Methods for Polynomial Regression Applied to the Analysis of Congruence

In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. Although this method is increasingly applied in congruence research, its complexity relative to other methods for assessing congruence (e.g., difference score methods) was one of the main claims against its use. The objective of this work is to gain additional insight into the use of polynomial regression in the area of social and behavioral sciences. First, we elaborate on the meaning and the inference based on the coefficients obtained by applying polynomial regression and explain the mathematical terms that are used to describe response surfaces. Then, we introduce additional inference methods and illustrate their application on a real-life example from the area of supportive exchanges using data collected by the Smithers Institute at Cornell University (supported by the National Institute on Alcohol Abuse and Alcoholism). Our work is aimed to provide a guide to researchers who apply polynomial regression in order to assess the effect of congruence between two constructs and enable better understanding and interpretation of the results obtained.

Statistical significance criteria for the rWG and average deviation interrater agreement indices

Despite the widespread use of interrater agreement statistics for multilevel modeling and other types of research, the existing guidelines for inferring the statistical significance of interrater agreement are quite limited. They are largely relevant only under conditions that numerous researchers have argued rarely exist. Here we address this problem by generating guidelines for inferring statistical significance under a number of conditions via a computer simulation. As a set, these guidelines cover many of the conditions researchers commonly face. We discuss how researchers can use the guidelines presented to more reasonably infer the statistical significance of interrater agreement relative to using the limited guidelines available in the extant literature.

Efficacy of oral methotrexate in paediatric Crohn’s disease: a multicentre propensity score study

Background Oral methotrexate (MTX) administration avoids weekly injections, reduces costs and may improve quality of life of patients with Crohn’s disease (CD), especially children. Routes of administration have never been systematically compared in CD. We aimed to compare effectiveness and safety of orally (PO) versus subcutaneously (SC) administered MTX in paediatric CD. Methods 226 children with CD treated with oral or subcutaneous MTX were included in a multicentre, retrospective 1-year cohort study (62% boys, mean age 13.8±2.8 years, 88% previous thiopurines). 38 (17%) were initially commenced on oral, 98 (43%) started subcutaneous and switched to oral and 90 (40%) were treated with subcutaneous only. Matching and ‘doubly robust’ weighted regression models were based on the propensity score method, controlling for confounding-by-indication bias. 11/23 pretreatment variables were different between the groups, but the propensity score modelling successfully balanced the treatment groups. Results 76 children (34%) had sustained steroid-free remission with a difference that did not reach significance between the PO and the SC groups (weighted OR=1.72 (95% CI 0.5 to 5.9); p=0.52). There were no differences in need for treatment escalation (p=0.24), elevated liver enzymes (p=0.59) or nausea (p=0.85). Height velocity was lower in the PO group (p=0.006) and time to remission was delayed in the PO group (p=0.036; Fleming (0, 1) test). Conclusions In this largest paediatric CD cohort to date, SC administered MTX was superior to PO, but only in some of the outcomes and with a modest effect size. Therefore, it may be reasonable to consider switching children in complete remission treated with subcutaneous MTX to the oral route with close monitoring of inflammatory markers and growth.

Establishment of Fetal Biometric Charts Using Quantile Regression Analysis

Fetal growth evaluation is an essential component of pregnancy surveillance. There have been several methods used to construct growth charts. The conventional charts used in current daily practice are based on small numbers and traditional statistical methods. The purpose of this study was to improve fetal biometric charts based on a much larger number of observations with an alternative statistical method: quantile regression analysis. A comparison between the charts is presented.

Screening for Sleep Disordered Breathing Among Applicants for a Professional Driverʼs License

Objectives: Individuals with sleep disordered breathing (SDB) are at high risk for falling asleep while driving. The aim of this study was to identify variables that would predict SDB among healthy young applicants for a professional drivers license. Method: A total of 301 applicants for a drivers license completed self-administered questionnaires. Sleep was recorded for one night with the Watch peripheral arterial tone–100. To identify possible predictors of SDB (Respiratory Disturbance Index >15), we employed new statistical methods. Results: The following items were identified as significant predictors of SDB: body mass index, age, Mini Sleep Questionnaire, smoking, father snoring, afternoon nap-taking, and falling asleep while traveling as a passenger. Moderate or severe SDB was prevalent in at least 25% of the applicants. Conclusion: New statistical methods revealed that a combination of questions related to sleep habits, complaints, and demographic data predicted most of the clinically significant SDB.

Significance Tests for Differences between Dependent Intraclass Correlation Coefficients (ICCs)

Most multi-level studies are cross-sectional and focus on a certain point in time, though various changes within levels may occur over time. This chapter presents a statistical method for assessing whether the degree of interdependency within a group has changed over time, using the intraclass correlation coefficient (ICC) as an indicator of the degree of homogeneity within the groups. It then shows how to apply this method using the SAS MIXED procedure. The problem was motivated by a study in which 120 subjects were divided into 40 groups of three. In a portion of the study, collective efficacy was the dependent variable measured for each subject under four different conditions (two levels of task interdependence at two points in time). ICC was used as a measure of group homogeneity with respect to collective efficacy, and the problem was how to compare the dependent ICCs associated with the different conditions.

Safety Management of Non-urban Roads in Israel: An Application of Empirical Bayes Evaluation

A safety management system was established to provide for continuously improved safety levels of the non-urban roads in Israel. One of the main functions of the system lies in the identification and treatment of HL (hazardous locations) on existing roads. In line with the state-of-the art in road safety, the HL identification is based on an empirical Bayes evaluation, where an HL is recognized using a high positive difference between the number of accidents expected at the site and that predicted for similar sites. The latter is estimated using safety performance functions that were developed for local conditions, including singleand dual-carriageway road sections, and various types of intersections: signalized/non-signalized, threeand four-legged. The procedure of HL identification is applied annually, serving as a basis for the working programs on road infrastructure improvements. Positive safety effects of such improvements were recently reported in the country. These activities comply with the Road Infrastructure Safety Directive that was recently introduced in the European Union.