228 resultados para Instrument variable regression


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Two rat monoclonal antibodies (mAbs), 44-22-1 and 46-6B5, which recognize an alloreactive cytotoxic clone, 3F9, have been further tested on a panel of T hybridomas and cytotoxic T-cell clones for binding and functional activities. The mAbs recognized only those cells sharing the expression of the T-cell receptor beta-chain variable region gene V beta 6 with 3F9. All V beta 6+ cells were activated by these mAbs under cross-linking conditions and their antigen-specific activation was blocked by soluble mAb. Furthermore, depletion of 46-6B5+ normal lymph node T cells eliminated all cells expressing the epitope recognized by 44-22-1 and V beta 6 mRNA.

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During the past twenty years, various instruments have been developed for the assessment of substance use in adolescents, mainly in the United States. However, few of them have been adapted to, and validated in, French-speaking populations. Consequently, although increasing alcohol and drug use among teenagers has become a major concern, the various health and social programs developed in response to this specific problem have received little attention with regard to follow-up and outcome assessment. A standardized multidimensional assessment instrument adapted for adolescents is needed to assess the individual needs of adolescents and assign them to the most appropriate treatment setting, to provide a single measurement within and across health and social systems, and to conduct treatment outcome evaluations. Moreover, having an available instrument makes it possible to develop longitudinal and transcultural research studies. For this reason, a French version of the Adolescent Drug Abuse Diagnosis (ADAD) was developed and validated at the University Child and Adolescent Psychiatric Clinic in Lausanne, Switzerland. This article aims to discuss the methodological issues that we faced when using the ADAD instrument in a 4-year longitudinal study including adolescent substance users. Methodological aspects relating to the content and format of the instrument, the assessment administration and the statistical analyses are discussed.

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Background: Thin melanomas (Breslow thickness <= 1 mm) are considered highly curable. The aim of this study was to evaluate the correlation between histological tumour regression and sentinel lymph node (SLN) involvement in thin melanomas. Patients and methods: This was a retrospective single-centre study of 34 patients with thin melanomas undergoing SLN biopsy between April 1998 and January 2005. Results: The study included 14 women and 20 men of mean age 56.3 years. Melanomas were located on the neck (n = 3), soles (n = 4), trunk (n = 13) and extremities (n = 14). Pathological examination showed 25 SSM, four acral lentiginous melanomas, three in situ melanomas, one nodular melanoma and one unclassified melanoma with a mean Breslow thickness of 0.57 mm. Histological tumour regression was observed in 26 over 34 cases and ulceration was found in one case. Clark levels were as follows: I (n = 3), II (n = 20), III (n = 9), IV (n = 2). Growth phase was available in 15 cases (seven radial and eight vertical). Mitotic rates, available in 24 cases, were: 0 (n = 9), 1 (n = 11), 2 (n = 2), 3 (n = 1), 6 (n = 1). One patient with histological tumour regression (2.9% of cases and 3.8% of cases with regressing tumours) had a metastatic SLN. One patient negative for SLN had a lung relapse and died of the disease. Mean follow-up was 26.2 months. Conclusion: The results of the present study and the analysis of the literature show that histological regression of the primary tumour does not seem predictive of higher risk of SLN involvement in thin melanomas. This suggests that screening for SLN is not indicated in thin melanomas, even those with histological regression.

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OBJECTIVE: To investigate the evolution of delirium of nursing home (NH) residents and their possible predictors. DESIGN: Post-hoc analysis of a prospective cohort assessment. SETTING: Ninety NHs in Switzerland. PARTICIPANTS: Included 14,771 NH residents. MEASUREMENTS: The Resident Assessment Instrument Minimum Data Set and the Nursing Home Confusion Assessment Method were used to determine follow-up of subsyndromal or full delirium in NH residents using discrete Markov chain modeling to describe long-term trajectories and multiple logistic regression analyses to determine predictors of the trajectories. RESULTS: We identified four major types of delirium time courses in NH. Increasing severity of cognitive impairment and of depressive symptoms at the initial assessment predicted the different delirium time courses. CONCLUSION: More pronounced cognitive impairment and depressive symptoms at the initial assessment are associated with different subsequent evolutions of delirium. The presence and evolution of delirium in the first year after NH admission predicted the subsequent course of delirium until death.

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OBJECTIVE: To identify predictors of nonresponse to a self-report study of patients with orthopedic trauma hospitalized for vocational rehabilitation between November 15, 2003, and December 31, 2005. The role of biopsychosocial complexity, assessed using the INTERMED, was of particular interest. DESIGN: Cohort study. Questionnaires with quality of life, sociodemographic, and job-related questions were given to patients at hospitalization and 1 year after discharge. Sociodemographic data, biopsychosocial complexity, and presence of comorbidity were available at hospitalization (baseline) for all eligible patients. Logistic regression models were used to test a number of baseline variables as potential predictors of nonresponse to the questionnaires at each of the 2 time points. SETTING: Rehabilitation clinic. PARTICIPANTS: Patients (N=990) hospitalized for vocational rehabilitation over a period of 2 years. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Nonresponse to the questionnaires was the binary dependent variable. RESULTS: Patients with high biopsychosocial complexity, foreign native language, or low educational level were less likely to respond at both time points. Younger patients were less likely to respond at 1 year. Those living in a stable partnership were less likely than singles to respond at hospitalization. Sex, psychiatric, and somatic comorbidity and alcoholism were never associated with nonresponse. CONCLUSIONS: We stress the importance of assessing biopsychosocial complexity to predict nonresponse. Furthermore, the factors we found to be predictive of nonresponse are also known to influence treatment outcome and vocational rehabilitation. Therefore, it is important to increase the response rate of the groups of concern in order to reduce selection bias in epidemiologic investigations.

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The risk of adverse psychological outcomes in adult victims of childhood and adolescent sexual abuse (CSA) has been documented; however, research on possible mediating variables is still required, namely with a clinical perspective. The attachment literature suggests that secure interpersonal relationships may represent such a variable. Twenty-eight women who had experienced episodes of CSA, and 16 control women, were interviewed using Bremner's Early Trauma Inventory and the DSM-IV Global Assessment of Functioning; they also responded to Collins' Relationship Scales Questionnaire, evaluating adult attachment representations in terms of Closeness, Dependence and Anxiety. Subjects with an experience of severe abuse reported significantly more interpersonal distance in relationships (low index of Closeness) than other subjects. The index of psychopathological functioning was correlated with both the severity of abuse and attachment (low index of Closeness). Regression analysis on the sample of abused women revealed that attachment predicted psychopathology when abuse was controlled for, whereas abuse did not predict psychopathology when attachment was controlled for. Therefore, preserving a capacity for closeness with attachment figures in adulthood appears to mediate the consequences of CSA on subsequent psychopathological outcome.

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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.

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Common variable immune deficiency is the most frequent primary immune deficiency, characterized mainly by a disorder of B lymphocytes differentiation and a deficit in immunoglobulins. The clinical manifestations include recurrent infections, non-infectious lung and digestive involvements, autoimmune diseases, and an increased susceptibility to cancers. Recent breakthroughs have been made in the understanding of some genetic mechanisms of the disease. Replacement therapy with intravenous immunoglobulins remains the treatment of choice, which allows significant improvement in the survival and quality of life. However progress should be made in the understanding of the pathophysiology and in the early detection of this disease, since a delay in the diagnosis may have harmful consequences in terms of morbidity and mortality.

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PURPOSE: The aim of this study was to compare VO2 kinetics during constant power cycle exercise measured using a conventional facemask (CM) or a respiratory snorkel (RS) designed for breath-by-breath analysis in swimming. METHODS: VO2 kinetics parameters-obtained using CM or RS, in randomized counterbalanced order-were compared in 10 trained triathletes performing two submaximal heavy-intensity cycling square-wave transitions. These VO2 kinetics parameters (ie, time delay: td1, td2; time constant: τ1, τ2; amplitude: A1, A2, for the primary phase and slow component, respectively) were modeled using a double exponential function. In the case of the RS data, this model incorporated an individually determined snorkel delay (ISD). RESULTS: Only td1 (8.9 ± 3.0 vs 13.8 ± 1.8 s, P < .01) differed between CM and RS, whereas all other parameters were not different (τ1 = 24.7 ± 7.6 vs 21.1 ± 6.3 s; A1 = 39.4 ± 5.3 vs 36.8 ± 5.1 mL x min(-1) x kg(-1); td2 = 107.5 ± 87.4 vs 183.5 ± 75.9 s; A2' (relevant slow component amplitude) = 2.6 ± 2.4 vs 3.1 ± 2.6 mL x min(-1) x kg(-1) for CM and RS, respectively). CONCLUSIONS: Although there can be a small mixture of breaths allowed by the volume of the snorkel in the transition to exercise, this does not appear to significantly influence the results. Therefore, given the use of an ISD, the RS is a valid instrument for the determination of VO2 kinetics within submaximal exercise.

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Introduction: There is little information regarding compliance with dietary recommendations in Switzerland. Objectives: To assess the trends in compliance with dietary recommendations in the Geneva population for period 1999 - 2009. Methods: Ten cross-sectional, population-based surveys (Bus Santé study). Dietary intake was assessed using a self-administered, validated semi quantitative Food Frequency Questionnaire. Compliance with the Swiss Society for Nutrition recommendations for nutrient intake was assessed. In all 9320 participants aged 35 to 75 years (50% women) were included. Trends were assessed by logistic regression adjusting for age, smoking stats, education and nationality, using survey year as the independent variable. Results: After excluding participants with extreme intakes, the percentage of participants with a cholesterol consumption< 300 mg/day increased from 40.8% in 1999 to 43.6% in 2009 for men (multivariate-adjusted p for trend = 0.04) and from 57.8% to 61.4% in women (multivariate-adjusted p for trend = 0.06). Calcium intake > 1 g/day decreased from 53.3% to 46.0% in men and from 47.6% to 40.7% in women (multivariate-adjusted p for trend< 0.001). Adequate iron intake decreased from 68.3%to 65.3% in men and from 13.3% to 8.4% in women (multivariate-adjusted p for trend< 0.001). Conversely, no significant changes were observed for carbohydrates, protein, total fat (including saturated, monounsaturated and polyunsaturated fatty acids), fibre, vitamins D and A. Conclusion: Fewimprovements were noted in adherence to dietary recommendations in the Geneva population between 1999 and 2009. The low and decreasing prevalence of adequate calcium and iron intake are of concern.

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This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

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Attrition in longitudinal studies can lead to biased results. The study is motivated by the unexpected observation that alcohol consumption decreased despite increased availability, which may be due to sample attrition of heavy drinkers. Several imputation methods have been proposed, but rarely compared in longitudinal studies of alcohol consumption. The imputation of consumption level measurements is computationally particularly challenging due to alcohol consumption being a semi-continuous variable (dichotomous drinking status and continuous volume among drinkers), and the non-normality of data in the continuous part. Data come from a longitudinal study in Denmark with four waves (2003-2006) and 1771 individuals at baseline. Five techniques for missing data are compared: Last value carried forward (LVCF) was used as a single, and Hotdeck, Heckman modelling, multivariate imputation by chained equations (MICE), and a Bayesian approach as multiple imputation methods. Predictive mean matching was used to account for non-normality, where instead of imputing regression estimates, "real" observed values from similar cases are imputed. Methods were also compared by means of a simulated dataset. The simulation showed that the Bayesian approach yielded the most unbiased estimates for imputation. The finding of no increase in consumption levels despite a higher availability remained unaltered. Copyright (C) 2011 John Wiley & Sons, Ltd.