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Mixture Modeling (mixture + modeling)
Kinds of Mixture Modeling Selected AbstractsMixture Modeling for Genome-Wide Localization of Transcription FactorsBIOMETRICS, Issue 1 2007Sündüz Kele Summary Chromatin immunoprecipitation followed by DNA microarray analysis (ChIP-chip methodology) is an efficient way of mapping genome-wide protein,DNA interactions. Data from tiling arrays encompass DNA,protein interaction measurements on thousands or millions of short oligonucleotides (probes) tiling a whole chromosome or genome. We propose a new model-based method for analyzing ChIP-chip data. The proposed model is motivated by the widely used two-component multinomial mixture model of de novo motif finding. It utilizes a hierarchical gamma mixture model of binding intensities while incorporating inherent spatial structure of the data. In this model, genomic regions belong to either one of the following two general groups: regions with a local protein,DNA interaction (peak) and regions lacking this interaction. Individual probes within a genomic region are allowed to have different localization rates accommodating different binding affinities. A novel feature of this model is the incorporation of a distribution for the peak size derived from the experimental design and parameters. This leads to the relaxation of the fixed peak size assumption that is commonly employed when computing a test statistic for these types of spatial data. Simulation studies and a real data application demonstrate good operating characteristics of the method including high sensitivity with small sample sizes when compared to available alternative methods. [source] Developmental Trajectories of Impulsivity and Their Association With Alcohol Use and Related Outcomes During Emerging and Young Adulthood IALCOHOLISM, Issue 8 2010Andrew K. Littlefield Background:, Research has documented normative patterns of personality change during emerging and young adulthood that reflect decreases in traits associated with substance use, such as impulsivity. However, evidence suggests variability in these developmental changes. Methods:, This study examined trajectories of impulsivity and their association with substance use and related problems from ages 18 to 35. Analyses were based on data collected from a cohort of college students (N = 489), at high and low risk for AUDs, first assessed as freshmen at a large, public university. Results:, Mixture modeling identified five trajectory groups that differed in baseline levels of impulsivity and developmental patterns of change. Notably, the trajectory group that exhibited the sharpest declines in impulsivity tended to display accelerated decreases in alcohol involvement from ages 18 to 25 compared to the other impulsivity groups. Conclusion:, Findings highlight the developmental nature of impulsivity across emerging and young adulthood and provide an empirical framework to identify key covariates of individual changes of impulsivity. [source] Matching motivation enhancement treatment to client motivation: re-examining the Project MATCH motivation matching hypothesisADDICTION, Issue 8 2010Katie Witkiewitz ABSTRACT Aims The current study was designed to re-examine the motivation matching hypothesis from Project MATCH using growth mixture modeling, an analytical technique that models variation in individual drinking patterns. Design, setting and participants Secondary data analyses of data from Project MATCH (n = 1726), a large multi-site alcoholism treatment-matching study. Measurements Percentage of drinking days was the primary outcome measure, assessed from 1 month to 12 months following treatment. Treatment assignment, alcohol dependence symptoms and baseline percentage of drinking days were included as covariates. Findings The results provided support for the motivation matching hypothesis in the out-patient sample and among females in the aftercare sample: the majority of individuals with lower baseline motivation had better outcomes if assigned to motivation enhancement treatment (MET) compared to those assigned to cognitive behavioral treatment (CBT). In the aftercare sample there was a moderating effect of gender and alcohol dependence severity, whereby males with lower baseline motivation and greater alcohol dependence drank more frequently if assigned to MET compared to those assigned to CBT. Conclusions Results from the current study lend partial support to the motivation-matching hypothesis and also demonstrated the importance of moderating influences on treatment matching effectiveness. Based upon these findings, individuals with low baseline motivation in out-patient settings and males with low levels of alcohol dependence or females in aftercare settings may benefit more from motivational enhancement techniques than from cognitive,behavioral techniques. [source] Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of Group 14GENETIC EPIDEMIOLOGY, Issue S1 2009Berit Kerner Abstract Participants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single-nucleotide polymorphisms to genome-wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: (1) The additional information provided by longitudinal data may be useful in genetic analyses. (2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age-of-onset effects. (3) Analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multifactorial diseases. (4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome-wide single-nucleotide polymorphism data were also discussed. Genet. Epidemiol. 33 (Suppl. 1):S93,S98, 2009. © 2009 Wiley-Liss, Inc. [source] SMIXTURE: strategy for mixture model clustering of multivariate imagesJOURNAL OF CHEMOMETRICS, Issue 11-12 2005Thanh N. Tran Abstract SMIXTURE, a novel strategy for mixture model clustering of multivariate images, has been developed. Most other clustering approaches require good guesses of the number of components (clusters) and the initial statistical parameters. In our approach, the initial parameters are determined by agglomerative clustering on homogenous regions, identified by region growing segmentation. SMIXTURE can be used in both a normal situation of mixture modeling, where the density of a cluster is modeled by a single normal distribution; and in a more complex situation, where the density of a single cluster is a mixture of several normal sub-clusters. The method has proven to be very robust to noise/outliers, overlapping clusters, is reasonably fast and is suitable for moderate to large images. Copyright © 2006 John Wiley & Sons, Ltd. [source] School Engagement Trajectories and Their Differential Predictive Relations to DropoutJOURNAL OF SOCIAL ISSUES, Issue 1 2008Michel Janosz Although most theories draw upon the construct of school engagement in their conceptualization of the dropout process, research addressing its hypothesized prospective relation with dropout remains scarce and does not account for the academic and social heterogeneity of students who leave school prematurely. This study explores the reality of different life-course pathways of school engagement and their predictive relations to dropout. Using an accelerated longitudinal design, we used growth mixture modeling to generate seven distinct trajectories of school engagement with 12- to 16-year-old students (N = 13,300). A vast majority of students were classified into three stable trajectories, distinguishing themselves at moderate to very high levels of school engagement. We refer to these as developmentally normative pathways in light of their frequent occurrence and stability. Although regrouping only one-tenth of participants, four other nonnormative (or unexpected pathways) accounted for the vast majority of dropouts. Dropout risk was closely linked with unstable pathways of school engagement. We conclude by debating the delicate investment balance between universal strategies and more selective and differentiated strategies to prevent dropout. We also discuss the need to better understand why, within normative trajectories, some students with high levels of school engagement drop out of school. [source] Posttraumatic stress symptom trajectories in children living in families reported for family violence,JOURNAL OF TRAUMATIC STRESS, Issue 5 2009Nicole R. Nugent The present study examined latent class trajectories of posttraumatic stress disorder (PTSD) and associations between demographics, prior trauma, and reason for referral on class membership. Children ages 7,18 (n=201) were recruited for participation in the Navy Family Study following reports to the U.S. Navy's Family Advocacy Program (FAP). Initial interviews were conducted 2,6 weeks following FAP referral, with follow-ups conducted at 9,12, 18,24, and 36,40 months. Growth mixture modeling revealed two latent class trajectories: a resilient class and a persistent symptom class. Relative to youth in the resilient class, participants in the persistent symptom class were more likely to be older and to report exposure to a greater number of trauma experiences at Time 1. [source] Patterns of treatment response in chronic posttraumatic stress disorder: An application of latent growth mixture modelingJOURNAL OF TRAUMATIC STRESS, Issue 4 2005Peter Elliott This study attempts to differentiate groups of individuals who exhibit different patterns of recovery following treatment for chronic posttraumatic stress disorder (PTSD) and describes these groups in terms of relevant characteristics at program intake. A sample of 2,219 Vietnam veterans who had completed a 12-week treatment program was followed up at 6, 12, and 24 months post admission using self-report measures. With change in PTSD symptoms over time as the focus, latent growth mixture modeling was used to assign individual veterans to subgroups. A three-group solution provided the best account of the data. Two groups showed moderate and consistent improvement over time although the larger group (n = 1,380) began treatment with more PTSD symptoms and improved more quickly over time. The smallest group (n = 87) showed a substantially different trajectory, with almost no net change in symptom levels over the 24-month period. The groups also varied significantly in terms of their characteristics, with symptom severity and improvements over time reflecting greater comorbidity and younger age. The results have both research and clinical implications. [source] Within-individual discrimination on the Concealed Information Test using dynamic mixture modelingPSYCHOPHYSIOLOGY, Issue 2 2009Izumi Matsuda Abstract Whether an examinee has information about a crime is determined by the Concealed Information Test based on autonomic differences between the crime-related item and other control items. Multivariate quantitative statistical methods have been proposed for this determination. However, these require specific databases of responses, which are problematic for field application. Alternative methods, using only an individual's data, are preferable, but traditionally such within-individual approaches have limitations because of small data sample size. The present study proposes a new within-individual judgment method, the hidden Markov discrimination method, in which time series-data are modeled with dynamic mixture distributions. This method was applied to experimental data and showed sufficient potential in discriminating guilty from innocent examinees in a mock theft experiment compared with performance of previous methods. [source] Personality Risk Factors Associated with Trajectories of Tobacco UseTHE AMERICAN JOURNAL ON ADDICTIONS, Issue 6 2006Judith S. Brook EdD The purpose of this longitudinal, prospective study was to evaluate trajectories of smoking in a cohort of African-American and Puerto Rican young adults and describe personality and behavioral factors associated with specific smoking trajectory group membership. Participants consisted of African-American and Puerto Rican male and female young adults (n = 451, mean age 26) from an inner-city community. Data were collected at four time points over a period of 13 years using structured interviews. Interviews took place within the schools and the participants' homes. Scales with adequate psychometric properties were adapted from previously validated measures. Variables that were examined for this study came from the domains of internalizing behaviors, externalizing behaviors, drug use, and demographic information. Data were analyzed using latent growth mixture modeling to explore discrete smoking trajectories. Logistic regression analyses were then used to examine the risk factors associated with the various smoking trajectory groups. Four trajectory groups were determined to best fit the data: nonsmokers, maturing-out smokers, late-starting smokers, and early-starting continuous smokers. Subjects who were unconventional, experienced intrapersonal distress, and used alcohol and illegal drugs were more likely to belong to one of the smoking trajectory groups than to the nonsmoking group. The early-starting continuous group scored highest on these personal risk attributes. The long-term impact of unconventional behavior, intrapersonal distress, and drug use on developmental trajectories of smoking support the importance of early intervention and prevention. [source] | |||||||||||||