Evaluation of the Psychometric Properties of the Five Facet of Mindfulness Questionnaire.

ObjectiveThe Five Facet of Mindfulness Questionnaire (FFMQ) is widely used to assess mindfulness. The present study provides a psychometric evaluation of the FFMQ that includes item response theory (IRT) analyses and evaluation of item characteristic curves.MethodWe administered the FFMQ, the Beck Depression Inventory-II, the Ruminative Response Scale, and the Emotion Regulation Questionnaire to a heterogenous sample of 240 community-based adults. We estimated internal consistency reliability, item-scale correlations, categorical confirmatory factor analysis, and IRT graded response models for the FFMQ. We also estimated correlations among the FFMQ scales and correlations with the other measures included in the study.ResultsInternal consistency reliabilities for the five FFMQ scales were 0.82 or higher. A five-factor categorical model fit the data well. IRT-estimated item characteristic curves indicated that the five response options were monotonically ordered for most of the items. Product-moment correlations between simple-summated scoring and IRT scoring of the scales were 0.97 or higher.ConclusionsThe FFMQ accurately identifies varying levels of trait mindfulness. IRT-derived estimates will inform future adaptations to the FFMQ (e.g., briefer versions) and the development of future mindfulness instruments

Conservation Contracting in Heterogeneous Landscapes: an application to watershed protection with threshold constraints

A key issue in the design of land use policy is how to integrate information about spatially variable biophysical and economic conditions into a cost-effective conservation plan. Using common biophysical scoring methods, in combination with economic data and simple optimization methods, we illustrate how one can identify a set of priority land parcels for conservation investment. We also demonstrate a way in which conservation agencies can incorporate concerns about biophysical thresholds in the identification of their priority land parcels. We apply these methods using Geographic Information System data from a New York conservation easement acquisition initiative for water quality protection. Working Paper # 2002-01

Cyclooxygenase-2 Expression in Bladder Cancer and Patient Prognosis: Results from a Large Clinical Cohort and Meta-Analysis

Aberrant overexpression of cyclooxygenase-2 (COX2) is observed in urothelial carcinoma of the bladder (UCB). Studies evaluating COX2 as a prognostic marker in UCB report contradictory results. We determined the prognostic potential of COX2 expression in UCB and quantitatively summarize the results with those of the literature through a meta-analysis. Newly diagnosed UCB patients recruited between 1998–2001 in 18 Spanish hospitals were prospectively included in the study and followed-up (median, 70.7 months). Diagnostic slides were reviewed and uniformly classified by expert pathologists. Clinical data was retrieved from hospital charts. Tissue microarrays containing non-muscle invasive (n = 557) and muscle invasive (n = 216) tumours were analyzed by immunohistochemistry using quantitative image analysis. Expression was evaluated in Cox regression models to assess the risk of recurrence, progression and disease-specific mortality. Meta-hazard ratios were estimated using our results and those from 11 additional evaluable studies. COX2 expression was observed in 38% (211/557) of non-muscle invasive and 63% (137/216) of muscle invasive tumors. Expression was associated with advanced pathological stage and grade (p<0.0001). In the univariable analyses, COX2 expression - as a categorical variable - was not associated with any of the outcomes analyzed. As a continuous variable, a weak association with recurrence in non-muscle invasive tumors was observed (p-value = 0.048). In the multivariable analyses, COX2 expression did not independently predict any of the considered outcomes. The meta-analysis confirmed these results. We did not find evidence that COX2 expression is an independent prognostic marker of recurrence, progression or survival in patients with UCB.The work was partially supported by the Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, Ministry of Science and Innovation, Spain (G03/174, 00/0745, PI051436, PI061614 and G03/174); Red Temática de Investigación Cooperativa en Cáncer- RD06/0020-RTICC; Consolider ONCOBIO; EU-FP6-STREP-37739-DRoP-ToP; EU-FP7-HEALTH-F2-2008-201663-UROMOL; EU-FP7-HEALTH-F2-2008-201333-DECanBio; USA-NIH-RO1-CA089715; and a PhD fellowship awarded to MJC from the ‘‘la Caixa’’ foundation, Spain, and a postdoctoral fellowship awarded to AFSA from the Fundación Científica de la AEC

Does segmentation always improve model performance in credit scoring?

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache

Synergistic drug combinations from electronic health records and gene expression.

ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing

Are autistic traits measured equivalently in individuals with and without an Autism Spectrum Disorder?:An invariance analysis of the Autism Spectrum Quotient Short Form

It is common to administer measures of autistic traits to those without autism spectrum disorders (ASDs) with, for example, the aim of understanding autistic personality characteristics in non-autistic individuals. Little research has examined the extent to which measures of autistic traits actually measure the same traits in the same way across those with and without an ASD. We addressed this question using a multi-group confirmatory factor invariance analysis of the Autism Quotient Short Form (AQ-S: Hoekstra et al. in J Autism Dev Disord 41(5):589-596, 2011) across those with (n = 148) and without (n = 168) ASD. Metric variance (equality of factor loadings), but not scalar invariance (equality of thresholds), held suggesting that the AQ-S measures the same latent traits in both groups, but with a bias in the manner in which trait levels are estimated. We, therefore, argue that the AQ-S can be used to investigate possible causes and consequences of autistic traits in both groups separately, but caution is due when combining or comparing levels of autistic traits across the two group