Modeling trend changes of mean annual exposure to PM2.5 particles in the Middle East countries via growth mixture models

2021 ◽  
Vol 14 (7) ◽  
Author(s):  
Gashtasb Mardani ◽  
Mahdiyeh Alikhani Faradonbeh ◽  
Zahra Fatahian Kelishadrokhi ◽  
Hadi Raeisi Shahraki
Keyword(s):  
Middle East ◽  
Mixture Models ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Middle East Countries

81 Identifying and predicting novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models

2019 ◽  
Author(s):  
Claire Deakin ◽  
Charalampia Papadopoulou ◽  
Muthana Al Obaidi ◽  
Clarissa Pilkington ◽  
Lucy Wedderburn ◽  
...  
Keyword(s):  
Mixture Models ◽  
Juvenile Dermatomyositis ◽  
Growth Mixture Models ◽  
Growth Mixture

Transition through smoking stages in adolescents: an innovative perspective using growth mixture models

2021 ◽  
Author(s):  
Asghar MohammadpourAsl ◽  
Nazanin Masoudi ◽  
Nasrin Jafari ◽  
Samane Yaghoubi ◽  
Farzaneh Hamidi ◽  
...  
Keyword(s):  
Mixture Models ◽  
Growth Mixture Models ◽  
Growth Mixture

scholarly journals Class enumeration false positive in skew-t family of continuous growth mixture models

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0231525
Author(s):  
Kiero Guerra-Peña ◽  
Zoilo Emilio García-Batista ◽  
Sarah Depaoli ◽  
Luis Eduardo Garrido
Keyword(s):  
Mixture Models ◽  
False Positive ◽  
Continuous Growth ◽  
Growth Mixture Models ◽  
Class Enumeration ◽  
Growth Mixture

scholarly journals Identification and prediction of novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models

Rheumatology ◽  
2020 ◽  
Author(s):  
Claire T Deakin ◽  
Charalampia Papadopoulou ◽  
Liza J McCann ◽  
Neil Martin ◽  
Muthana Al-Obaidi ◽  
...  
Keyword(s):  
Disease Activity ◽  
Mixture Models ◽  
Clinical Features ◽  
Lasso Regression ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Class 1 ◽  
Time Since Diagnosis

Abstract Objectives Uncertainty around clinical heterogeneity and outcomes for patients with JDM represents a major burden of disease and a challenge for clinical management. We sought to identify novel classes of patients having similar temporal patterns in disease activity and relate them to baseline clinical features. Methods Data were obtained for n = 519 patients, including baseline demographic and clinical features, baseline and follow-up records of physician’s global assessment of disease (PGA), and skin disease activity (modified DAS). Growth mixture models (GMMs) were fitted to identify classes of patients with similar trajectories of these variables. Baseline predictors of class membership were identified using Lasso regression. Results GMM analysis of PGA identified two classes of patients. Patients in class 1 (89%) tended to improve, while patients in class 2 (11%) had more persistent disease. Lasso regression identified abnormal respiration, lipodystrophy and time since diagnosis as baseline predictors of class 2 membership, with estimated odds ratios, controlling for the other two variables, of 1.91 for presence of abnormal respiration, 1.92 for lipodystrophy and 1.32 for time since diagnosis. GMM analysis of modified DAS identified three classes of patients. Patients in classes 1 (16%) and 2 (12%) had higher levels of modified DAS at diagnosis that improved or remained high, respectively. Patients in class 3 (72%) began with lower DAS levels that improved more quickly. Higher proportions of patients in PGA class 2 were in DAS class 2 (19%, compared with 16 and 10%). Conclusion GMM analysis identified novel JDM phenotypes based on longitudinal PGA and modified DAS.

scholarly journals A Comparison of Different Nonnormal Distributions in Growth Mixture Models

2019 ◽  
Vol 79 (3) ◽  
pp. 577-597
Author(s):  
Sookyoung Son ◽  
Hyunjung Lee ◽  
Yoona Jang ◽  
Junyeong Yang ◽  
Sehee Hong
Keyword(s):  
Mixture Models ◽  
Outcome Variable ◽  
Normal Distributions ◽  
Nonnormal Distributions ◽  
Growth Mixture Models ◽  
Time Points ◽  
Growth Mixture ◽  
Distal Outcome ◽  
T Distribution ◽  
Skew Normal

The purpose of the present study is to compare nonnormal distributions (i.e., t, skew-normal, skew- t with equal skew and skew- t with unequal skew) in growth mixture models (GMMs) based on diverse conditions of a number of time points, sample sizes, and skewness for intercepts. To carry out this research, two simulation studies were conducted with two different models: an unconditional GMM and a GMM with a continuous distal outcome variable. For the simulation, data were generated under the conditions of a different number of time points (4, 8), sample size (300, 800, 1,500), and skewness for intercept (1.2, 2, 4). Results demonstrate that it is not appropriate to fit nonnormal data to normal, t, or skew-normal distributions other than the skew- t distribution. It was also found that if there is skewness over time, it is necessary to model skewness in the slope as well.

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