Further development of a diagnostic platform using BAGS-assigned subtypes may allow pathogenetic studies to improve disease management.

Objectives: To estimate the cumulative incidence of arthritis-induced orofacial symptoms, dysfunctions, and dentofacial deformities in growing individuals with juvenile idiopathic arthritis (JIA) in a 36 month regional cohort study, and to identify predictors for the development of arthritis-induced dentofacial deformities.Methods: Data was retrieved from the Aarhus JIA TMJ cohort register, which contains standardized, longitudinal, observational data regarding orofacial conditions in patients with JIA (n=1040). This regional cohort represents the majority of all subjects with JIA from the western part of Denmark between 1990 and 2016, regardless of temporomandibular joint (TMJ) arthritis status. Cumulative incidences of orofacial conditions were reported using Kaplan-Meier methods and predictors for dentofacial deformity was identified using Cox proportional hazard analysis.Results: Follow-up data from 351 subjects over thirty-six months was included in this study.Median age at first clinical examination was 6.6 years (25/75 centiles: 4.9 and 9.9 years).Orofacial symptoms and dysfunction were common findings at 36 months after the first clinical examination and approximately five years after JIA onset, with a cumulative incidence of 38% and 53%, respectively. Dentofacial deformities were found in 35% of

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Methods for clustering in unsupervised learning are an important part of the statistical toolbox in numerous scientific disciplines. Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian mixture copula models (GMCM) for general unsupervised learning based on clustering. Li, Brown, Huang, and Bickel (2011) independently discussed a special case of these GMCMs as a novel approach to meta-analysis in highdimensional settings. GMCMs have attractive properties which make them highly flexible and therefore interesting alternatives to other well-established methods. However, parameter estimation is hard because of intrinsic identifiability issues and intractable likelihood functions. Both aforementioned papers discuss similar expectation-maximization-like algorithms as their pseudo maximum likelihood estimation procedure. We present and discuss an improved implementation in R of both classes of GMCMs along with various alternative optimization routines to the EM algorithm. The software is freely available in the R package GMCM. The implementation is fast, general, and optimized for very large numbers of observations. We demonstrate the use of package GMCM through different applications.