Results 31 to 40 of about 450,220 (290)

Growth mixture modeling with non‐normal distributions [PDF]

Statistics in Medicine, 2014
A limiting feature of previous work on growth mixture modeling is the assumption of normally distributed variables within each latent class. With strongly non‐normal outcomes, this means that several latent classes are required to capture the observed variable distributions. Being able to relax the assumption of within‐class normality has the advantage
Bengt, Muthén, Tihomir, Asparouhov
openaire   +2 more sources

Demand for Medical Care by the Elderly: A Nonparametric Variational Bayesian Mixture Approach [PDF]

, 2018
Outpatient care is a large share of total health care spending, making analysis of data on outpatient utilization an important part of understanding patterns and drivers of health care spending growth.
Holle, Rolf, Kurz, Christoph F.
core   +1 more source

Identifying Longitudinal CD4:CD8 Ratio Trajectories Indicative of Chronic Renal Disease Risk among People Living with HIV: An Application of Growth Mixture Models

Viruses, 2023
The incidence of chronic kidney disease (CKD) is increasing among people living with HIV (PLWH). Routine monitoring of indicators such as CD4:CD8 ratio might improve the early detection of CKD.
Alejandra Fonseca-Cuevas, Patrick Newsome, Lu Wang, Michelle Y. Chen, Chris G. Richardson, Mark Hull, Taylor McLinden, Silvia Guillemi, Rolando Barrios, Julio S. G. Montaner, Viviane D. Lima   +10 more
doaj   +1 more source

Trajectories of university adjustment in the United Kingdom: Emotion management and emotional self-efficacy protect against initial poor adjustment [PDF]

, 2013
Little is known about individual differences in the pattern of university adjustment. This study explored longitudinal associations between emotional self-efficacy, emotion management, university adjustment, and academic achievement in a sample of first ...
Austin, Austin, Baker, Bandura, Bandura, Beyers, Brackett, Christie, Cooke, Crombag, Dacre-Pool, Dacre-Pool, Dahmus, Davis, Duchesne, Duncan, Engelberg, Galatzer-Levy, Gall, Gerdes, Hu, Keefer, Keefer, Kirk, L. Barnes, L. Dacre-Pool, Little, Little, Lyons, MacCann, MacCann, Marsh, Mayer, McLachlan, Muthén, Muthén, Muthén, Muthén, Nagin, Nelis, P. Qualter, Parker, Parker, Petersen, Petrides, Petrides, Prillerman, Pritchard, Qualter, R.A. Harris, Radloff, Rickinson, Rickinson, Russell, S. Roberts, S.M. Nightingale, Saklofske, Schafer, Sennett, Soucy, Strahan, Swanson, Tao, Tariq, V. Tariq, van Rooijen, Vlaander, Weko, Y. Appleby   +68 more
core   +1 more source

Latent classes of nonresponders, rapid responders, and gradual responders in depressed outpatients receiving antidepressant medication and psychotherapy [PDF]

, 2015
BackgroundWe used growth mixture modeling (GMM) to identify subsets of patients with qualitatively distinct symptom trajectories resulting from treatment. Existing studies have focused on 12-week antidepressant trials.
Bagby, Michael, De Bolle, Marleen, De Fruyt, Filip, Quilty, Lena, Rouillon, Frederic, Thibodeau, Michel   +5 more
core   +2 more sources

General growth mixture modeling for randomized preventive interventions [PDF]

Biostatistics, 2002
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 ...
Muthen, Bengt, Brown, C. H., Masyn, Katherine, Jo, Booil, Khoo, Siek-Toon, Yang, Chih-Chien, Wang, Chen-Pin, Kellam, Sheppard G., Carlin, John B, Liao, Jason   +9 more
openaire   +3 more sources

Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Obesity Science & Practice, 2021
Background Group‐based trajectory modeling has been applied to identify distinct trajectories of growth across the life course. Objectives of this study were to describe the methodological approaches for group‐based modeling of growth across the life ...
Vanessa De Rubeis, Alessandra T. Andreacchi, Isobel Sharpe, Lauren E. Griffith, Charles D. G. Keown‐Stoneman, Laura N. Anderson   +5 more
doaj   +1 more source

Influences of Covariates on Growth Mixture Modeling [PDF]

Journal of Drug Issues, 2010
This study investigated the influence of including a covariate and/or a distal outcome on growth mixture modeling (GMM). GMM was used to examine patterns of days of heroin use over 16 years among 471 heroin users and the relationship of those patterns to mortality (distal outcome).
David, Huang, Mary-Lynn, Brecht, Motoaki, Hara, Yih-Ing, Hser   +3 more
openaire   +2 more sources

On growth curves and mixture models [PDF]

Infant and Child Development, 2006
AbstractThe multilevel model of change and the latent growth model are flexible means to describe all sorts of population heterogeneity with respect to growth and development, including the presence of sub‐populations. The growth mixture model is a natural extension of these models. It comes at hand when information about sub‐populations is missing and
Hoeksma, J.B., Kelderman, H.
openaire   +2 more sources

Development of self-control in early childhood—a growth mixture modeling approach

Cogent Psychology, 2018
Self-control emerges in early childhood and is shown to be strongly related to poor adulthood outcomes. The development of self-control was long believed to be homogeneous among individuals and stable in rank.
Qianqian Pan, Qingqing Zhu
doaj   +1 more source