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Generalized Linear Modeling (generalized + linear_modeling)
Selected AbstractsRACE, ETHNICITY, THREAT AND THE LABELING OF CONVICTED FELONS,CRIMINOLOGY, Issue 3 2005STEPHANIE BONTRAGER Florida law allows judges to withhold adjudication of guilt for persons who have either pled guilty or been found guilty of a felony. This provision may apply only to persons who will be sentenced to probation, and it allows such individuals to retain all civil rights and to truthfully assert they had not been convicted of a felony. This paper examines the effects of race and Hispanic ethnicity on the withholding of adjudication for 91,477 males sentenced to probation in Florida between 1999 and 2002. Hierarchical Generalized Linear Modeling is used to assess the direct effects of defendant attributes as well as the cross-level interactions between race, ethnicity and community level indicators of threat, such as percentage black and Hispanic and concentrated disadvantage. Our results show that Hispanics and blacks are significantly less likely to have adjudication withheld when other individual and community level factors are controlled. This effect is especially pronounced for blacks and for drug offenders. Cross-level interactions show that concentrated disadvantage has a substantial effect on the adjudication withheld outcome for both black and Hispanic defendants. The implications of these results for the conceptualization of racial/ethnic threat at the individual, situational and social levels are discussed. [source] Associations of ALDH2 and ADH1B Genotypes With Alcohol-Related Phenotypes in Asian Young AdultsALCOHOLISM, Issue 5 2009Christian S. Hendershot Background:, Associations of ALDH2 and ADH1B genotypes with alcohol use have been evaluated largely using case,control studies, which typically focus on adult samples and dichotomous diagnostic outcomes. Relatively fewer studies have evaluated ALDH2 and ADH1B in relation to continuous drinking outcomes or at different developmental stages. This study examined additive and interactive effects of ALDH2 and ADH1B genotypes on drinking behavior in a mixed-gender sample of Asian young adults, focusing on continuous phenotypes (e.g., heavy episodic and hazardous drinking, alcohol sensitivity, drinking consequences) whose expression is expected to precede the onset of alcohol use disorders. Methods:, The sample included 182 Chinese- and Korean-American young adults ages 18 years and older (mean age = 20 years). Effects of ALDH2, ADH1B and ethnicity were estimated using generalized linear modeling. Results:, The ALDH2*2 allele predicted lower reported rates of alcohol use and drinking consequences as well as greater reported sensitivity to alcohol. There were significant ethnic group differences in drinking outcomes, such that Korean ethnicity predicted higher drinking rates and lower alcohol sensitivity. ADH1B status was not significantly related to drinking outcomes. Conclusions:, Ethnicity and ALDH2 status, but not ADH1B status, consistently explained significant variance in alcohol consumption in this relatively young sample. Results extend previous work by showing an association of ALDH2 genotype with drinking consequences. Findings are discussed in the context of possible developmental and population differences in the influence of ALDH2 and ADH1B variations on alcohol-related phenotypes. [source] Subjective Well-Being and PeaceJOURNAL OF SOCIAL ISSUES, Issue 2 2007Ed Diener Hierarchical generalized linear modeling was employed to examine the relations between person-level subjective well-being (SWB) and peace-relevant attitudes, and how these relations vary across nations in the World Values Survey. Person-level SWB was associated with more confidence in the government and armed forces, greater emphasis on postmaterialist values, stronger support for democracy, less intolerance of immigrants and racial groups, and greater willingness to fight for one's country. These associations were moderated at the nation level by liberal development, violent inequality, gross domestic product, and nation-level SWB. The moderator effects indicate that happy people are not completely blind to the conditions of their society and that their endorsement of peace attitudes is sensitive to whether the conditions for peace do exist. [source] Comparison of linear predictors obtained by data transformation, generalized linear models (GLM) and response modeling methodology (RMM)QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 4 2008Haim Shore Abstract The data-transformation approach and generalized linear modeling both require specification of a transformation prior to deriving the linear predictor (LP). By contrast, response modeling methodology (RMM) requires no such specifications. Furthermore, RMM effectively decouples modeling of the LP from modeling its relationship to the response. It may therefore be of interest to compare LPs obtained by the three approaches. Based on numerical quality problems that have appeared in the literature, these approaches are compared in terms of both the derived structure of the LPs and goodness-of-fit statistics. The relative advantages of RMM are discussed. Copyright © 2007 John Wiley & Sons, Ltd. [source] Whole-body bone scintigraphy provides a measure of the total-body burden of osteoarthritis for the purpose of systemic biomarker validationARTHRITIS & RHEUMATISM, Issue 11 2009Shelby Addison Objective To evaluate the association of serum and synovial fluid cartilage oligomeric matrix protein (COMP) with systemic and local measures of osteoarthritis (OA) activity by bone scintigraphy. Methods Samples of serum and knee joint synovial fluid (275 knees) were obtained from 159 patients with symptomatic OA of at least 1 knee. Bone scintigraphy using 99mTc-labeled methylene diphosphonate was performed, and early-phase knee scans and late-phase whole-body bone scans of 15 additional joint sites were scored semiquantitatively. To control for within-subject correlations of knee data, generalized linear modeling was used in the correlation of the bone scan scores with the COMP levels. Principal components analysis was used to explore the contribution of each joint site to the variance in serum COMP levels. Results The correlation between synovial fluid and serum COMP levels was significant (r = 0.206, P = 0.006). Synovial fluid COMP levels correlated most strongly with the early-phase knee bone scan scores (P = 0.0003), even after adjustment for OA severity according to the late-phase bone scan scores (P = 0.015), as well as synovial fluid volumes (P < 0.0001). Serum COMP levels correlated with the total-body bone scan scores (r = 0.188, P = 0.018) and with a factor composed of the bone scan scores in the shoulders, spine, lateral knees, and sacroiliac joints (P = 0.0004). Conclusion Synovial fluid COMP levels correlated strongly with 2 indicators of knee joint inflammation: early-phase bone scintigraphic findings and synovial fluid volume. Serum COMP levels correlated with total-body joint disease severity as determined by late-phase bone scintigraphy, supporting the hypothesis that whole-body bone scintigraphy is a means of quantifying the total-body burden of OA for systemic biomarker validation. [source] Functional Generalized Linear Models with Images as PredictorsBIOMETRICS, Issue 1 2010Philip T. Reiss Summary Functional principal component regression (FPCR) is a promising new method for regressing scalar outcomes on functional predictors. In this article, we present a theoretical justification for the use of principal components in functional regression. FPCR is then extended in two directions: from linear to the generalized linear modeling, and from univariate signal predictors to high-resolution image predictors. We show how to implement the method efficiently by adapting generalized additive model technology to the functional regression context. A technique is proposed for estimating simultaneous confidence bands for the coefficient function; in the neuroimaging setting, this yields a novel means to identify brain regions that are associated with a clinical outcome. A new application of likelihood ratio testing is described for assessing the null hypothesis of a constant coefficient function. The performance of the methodology is illustrated via simulations and real data analyses with positron emission tomography images as predictors. [source] | ||||||||||