Biomedical subjects

Bengt Muthén

Publications and source records attributed to Bengt Muthén.

6 recordsLinked to original sources

PubMed2006

Should substance use disorders be considered as categorical or dimensional?

AIMS: This paper discusses the representation of diagnostic criteria using categorical and dimensional statistical models. Conventional modeling using categorical or continuous latent variables in the form of latent class analysis and factor (IRT) analysis has limitations for the analysis of diagnostic criteria. METHODS: New hybrid models are discussed which provide both categorical and dimensional representations in the same model using mixture models. Conventional and new models are applied and compared using recent data for Diagnostic and Statistical Manual of Mental Disorders version IV (DSM-IV) alcohol dependence and abuse criteria from the National Epidemiologic Survey on Alcohol and Related Conditions. Classification results from hybrid models are compared to the DSM-IV approach of using the number of diagnostic criteria fulfilled. RESULTS: It is found that new hybrid mixture models are more suitable than latent class and factor (IRT) models. CONCLUSIONS: Implications for DSM-V are discussed in terms of reporting results using both categories and dimensions.

Alcoholism↗

PubMed2006

Advances in behavioral genetics modeling using Mplus: applications of factor mixture modeling to twin data.

This article discusses new latent variable techniques developed by the authors. As an illustration, a new factor mixture model is applied to the monozygotic-dizygotic twin analysis of binary items measuring alcohol-use disorder. In this model, heritability is simultaneously studied with respect to latent class membership and within-class severity dimensions. Different latent classes of individuals are allowed to have different heritability for the severity dimensions. The factor mixture approach appears to have great potential for the genetic analyses of heterogeneous populations. Generalizations for longitudinal data are also outlined.

Adult↗

PubMed2005

Investigating population heterogeneity with factor mixture models.

Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A step-by-step analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point.

Child↗

PubMed2004

When the course of aggressive behavior in childhood does not predict antisocial outcomes in adolescence and young adulthood: an examination of potential explanatory variables.

Theoretical models and empirical studies suggest that there are a number of distinct pathways of aggressive behavior development in childhood that place youth at risk for antisocial outcomes in adolescence and young adulthood. The prediction of later antisocial behavior based on these early pathways, although substantial, is not perfect. The goal of the present study was to identify factors that explain why some boys on a high-risk developmental trajectory in middle childhood do not experience an untoward outcome, and, conversely, why some boys progressing on a low-risk trajectory do become involved in later antisocial behavior. To that end, we explored a set of theoretically derived predictors measured at entrance to elementary and middle school and examined their utility in explaining discordant cases. First-grade reading achievement, race, and poverty status proved to be significant early predictors of discordance, whereas the significant middle-school predictors were parent monitoring, deviant peer affiliation, and neighborhood level of deviant behavior.

Achievement↗

PubMed2003

Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003).

This commentary discusses the D. J. Bauer and P. J. Curran (2003) investigation of growth mixture modeling. Single-class modeling of nonnormal outcomes is compared with modeling with multiple latent trajectory classes. New statistical tests of multiple-class models are discussed. Principles for substantive investigation of growth mixture model results are presented and illustrated by an example of high school dropout predicted by low mathematics achievement development in Grades 7-10.

Humans↗

PubMed2002

General growth mixture modeling for randomized preventive interventions.

This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finite-mixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as non-normative growth. The analysis identifies subgroup membership and allows theory-based modeling of intervention effects in the different subgroups. An example is presented concerning a randomized intervention in Baltimore public schools aimed at reducing aggressive classroom behavior, where only students who were initially more aggressive showed benefits from the intervention.

Journal Article↗