scholarly journals O17 Modelling longitudinal patient-reported outcome measures in JDM

Rheumatology ◽  
2020 ◽  
Vol 59 (Supplement_2) ◽  
Author(s):  
Paris J Baptiste ◽  
Lucy R Wedderburn ◽  
Claire T Deakin ◽  
Bianca L. De Stavola ◽  
Edmund Njeru Njagi ◽  
...  
Keyword(s):  
Disease Activity ◽  
Mixture Models ◽  
Patient Reported Outcome ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Latent Classes ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Gastrointestinal Problems ◽  
Patient Reported

Abstract Background Juvenile dermatomyositis (JDM) is a rare autoimmune disease known to primarily cause rash and muscle weakness. The evolution of the disease is still unclear, in particular disease activity based on patient-reported outcomes. A cohort of 493 patients with 3,625 visits up to 5 years since diagnosis was used to explore disease trajectories based on the patient-reported outcome, patient/parent visual analogue scale (VAS), completed by the appropriate person depending on the child’s age. Age at diagnosis, sex, ethnicity and baseline physician's global assessment (PGA) measurements were considered as predictors of disease activity. In addition to this 8 baseline clinical/medical history variables were also considered as potentially predictive: ulcerations, Gottron’s papules, myalgia, fever, fatigue, dysphagia, respiration and gastrointestinal problems. Methods A mixed effects model was fitted to the data to identify the strongest predictors of disease activity accounting for correlations of patient/parent VAS measurements within patients. Growth mixture models were used to identify subgroups of patients that shared similar trajectories (latent classes) and logistic regression was used to predict the probability of belonging to the subgroup that had more severe disease activity. The identified latent classes of disease activity, based on the patient-reported outcome of patient/parent VAS, were compared with previously identified latent classes derived from PGA as the outcome measure. Results The results from fitting a mixed effects model showed that disease activity had a cubic relationship with time since diagnosis. Being non-white and having a history of myalgia and gastrointestinal problems was shown to predict higher disease activity across the whole follow-up time. The results from fitting growth mixture models led to identifying two classes: the first showed an improvement in condition after the first year, which correlated with results from the mixed effects model, the second, more severe class, was on average higher and showed little improvement across the 5 years. In addition to the predictors identified in the mixed effects model, skin ulceration and older than the mean age (8.3 years) at diagnosis were shown to be associated with the probability of belonging to the more severe class. Conclusion Comparing these results to those previously found in analyses of PGA data collected on the same patients, we found that the patterns of activity were similar although on average higher, indicating that reports of disease activity by patients/parents were worse than those collected from physicians. This could be due to factors influencing patient’s experiences that are not measured by physicians. Discussions with clinicians suggest that this could be due to symptoms that are difficult to measure and that are unaffected by treatment, for example, symptoms causing damage. These are often overlooked in physician’s assessments, despite being an important factor for patients. Disclosures P.J. Baptiste None. L.R. Wedderburn None. C.T. Deakin None. B.L. De Stavola None. E. Njagi None.

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 Covariance Pattern Mixture Models: Eliminating Random Effects to Improve Convergence and Performance

2019 ◽  
Author(s):  
Daniel McNeish ◽  
Jeffrey Harring
Keyword(s):  
Mixture Models ◽  
Random Effects ◽  
Convergence Rates ◽  
Empirical Studies ◽  
Population Heterogeneity ◽  
Latent Classes ◽  
Growth Mixture Models ◽  
Class Enumeration ◽  
Growth Mixture ◽  
And Performance

Growth mixture models (GMMs) are prevalent for modeling unknown population heterogeneity via distinct latent classes. However, GMMs are riddled with convergence issues, often requiring researchers to atheoretically alter the model with cross-class constraints to obtain convergence. We discuss how within-class random effects in GMMs exacerbate convergence issues even though these random effects rarely help to answer typical research questions. That is, latent classes provide a discretization of continuous random effects, so including additional random effects within latent classes can unnecessarily complicate the model. These random effects are commonly included to properly specify the marginal covariance; however, random effects are inefficient for patterning a covariance matrix, resulting in estimation issues. Such a goal can be achieved more simply covariance pattern models, which we extend to the mixture model context in this paper (covariance pattern mixture models, CPMMs). We provide evidence from theory, simulation, and an empirical example showing that employing CPMMs (even if misspecified) instead of GMMs can circumvent computational difficulties that can plague GMMs without sacrificing the ability to answer the type of questions commonly asked in empirical studies. Results show advantages of CPMMs with respect to improved class enumeration, and less biased class-specific growth trajectories in addition to vastly improved convergence rates. Results also show that constraining covariance parameters across classes to bypass convergence issues with GMMs leads to poor results. An extensive software appendix is included to assist researchers run CPMMs in Mplus.

scholarly journals Challenges in modelling the random structure correctly in growth mixture models and the impact this has on model mixtures

2014 ◽  
Vol 5 (3) ◽  
pp. 197-205 ◽  
Author(s):  
M. S. Gilthorpe ◽  
D. L. Dahly ◽  
Y.-K. Tu ◽  
L. D. Kubzansky ◽  
E. Goodman
Keyword(s):  
Mixture Models ◽  
Later Life ◽  
Random Structure ◽  
Latent Classes ◽  
Growth Mixture Models ◽  
Autocorrelation Structure ◽  
Growth Mixture ◽  
Ideal Number ◽  
The Impact ◽  
The Ideal

Lifecourse trajectories of clinical or anthropological attributes are useful for identifying how our early-life experiences influence later-life morbidity and mortality. Researchers often use growth mixture models (GMMs) to estimate such phenomena. It is common to place constrains on the random part of the GMM to improve parsimony or to aid convergence, but this can lead to an autoregressive structure that distorts the nature of the mixtures and subsequent model interpretation. This is especially true if changes in the outcome within individuals are gradual compared with the magnitude of differences between individuals. This is not widely appreciated, nor is its impact well understood. Using repeat measures of body mass index (BMI) for 1528 US adolescents, we estimated GMMs that required variance–covariance constraints to attain convergence. We contrasted constrained models with and without an autocorrelation structure to assess the impact this had on the ideal number of latent classes, their size and composition. We also contrasted model options using simulations. When the GMM variance–covariance structure was constrained, a within-class autocorrelation structure emerged. When not modelled explicitly, this led to poorer model fit and models that differed substantially in the ideal number of latent classes, as well as class size and composition. Failure to carefully consider the random structure of data within a GMM framework may lead to erroneous model inferences, especially for outcomes with greater within-person than between-person homogeneity, such as BMI. It is crucial to reflect on the underlying data generation processes when building such models.

scholarly journals Growth mixture models: a case example of the longitudinal analysis of patient‐reported outcomes data captured by a clinical registry

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jae-Yung Kwon ◽  
Richard Sawatzky ◽  
Jennifer Baumbusch ◽  
Sandra Lauck ◽  
Pamela A. Ratner
Keyword(s):  
Atrial Fibrillation ◽  
Longitudinal Analysis ◽  
Model Building ◽  
Population Based ◽  
Patient Reported Outcome ◽  
Latent Classes ◽  
Clinical Registry ◽  
Growth Mixture ◽  
Growth Mixture Modelling ◽  
Patient Reported

Abstract Background An assumption in many analyses of longitudinal patient-reported outcome (PRO) data is that there is a single population following a single health trajectory. One approach that may help researchers move beyond this traditional assumption, with its inherent limitations, is growth mixture modelling (GMM), which can identify and assess multiple unobserved trajectories of patients’ health outcomes. We describe the process that was undertaken for a GMM analysis of longitudinal PRO data captured by a clinical registry for outpatients with atrial fibrillation (AF). Methods This expository paper describes the modelling approach and some methodological issues that require particular attention, including (a) determining the metric of time, (b) specifying the GMMs, and (c) including predictors of membership in the identified latent classes (groups or subtypes of patients with distinct trajectories). An example is provided of a longitudinal analysis of PRO data (patients’ responses to the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire) collected between 2008 and 2016 for a population-based cardiac registry and deterministically linked with administrative health data. Results In determining the metric of time, multiple processes were required to ensure that “time” accounted for both the frequency and timing of the measurement occurrences in light of the variability in both the number of measures taken and the intervals between those measures. In specifying the GMM, convergence issues, a common problem that results in unreliable model estimates, required constrained parameter exploration techniques. For the identification of predictors of the latent classes, the 3-step (stepwise) approach was selected such that the addition of predictor variables did not change class membership itself. Conclusions GMM can be a valuable tool for classifying multiple unique PRO trajectories that have previously been unobserved in real-world applications; however, their use requires substantial transparency regarding the processes underlying model building as they can directly affect the results and therefore their interpretation.

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

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

scholarly journals AB0300 LUPUS DISEASE ACTIVITY CORRELATES WITH QUALITY OF LIFE BUT NOT WITH HEALTH LITERACY STATUS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1176.2-1176
Author(s):  
E. Eraslan ◽  
R. Bilici Salman ◽  
H. Satiş ◽  
A. Avanoglu Guler ◽  
H. Karadeniz ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Literacy ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Patient Reported Outcome ◽  
The Body ◽  
Limited Health Literacy ◽  
Patient Reported ◽  
Limited Health

Background:Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown etiology that can affect any organ of the body. SLE is associated with adverse effects on both health and non-health-related quality of life (HRQOL and non-HRQOL). Lupus PRO is a patient reported outcome measure that has been validated in many languages. It has 44 items that cover both HRQOL and non-HRQOL (1). Health literacy is defined as the degree to which individuals have the capacity to obtain, process and understand basic health information and services needed to make appropriate health decisions. Multiple studies indicate that people with limited health literacy have worse health status and higher rates of hospitalization (2).Objectives:We aimed to evaluate the relationship between the LLDAS (Lupus Low Disease Activity State) criteria and the Lupus PRO test, as well as the health literacy status of lupus patients.Methods:83 SLE patients (94% women) were included in the study. We performed Lupus PRO and the European Health Literacy Survey tests during the routine follow-up visits of lupus patients to our rheumatology outpatient clinic and admissions to rheumatology inpatient clinic. Available clinical data on medical records were obtained, physician global assessments (PGA) were recorded by the attending physician.Results:LLDAS criteria strongly and inversely correlated with the total score, as well as the mood subunit of the Lupus PRO. Similarly, it also significantly inversely correlated with the body appearence and goals subunits. Health literacy status of the patients did not correlate with their LLDAS scores, ie their disease activities.Conclusion:Our results suggest that lupus disease activity, assessed by LLDAS criteria, significantly correlates with measures of quality of life, spesicifically Lupus PRO test, but not with health literacy status. Further studies are needed to evaluate if health literacy is related with damage, hospitalization or mortality associated with lupus.References:[1]Jolly M, Pickard AS, Block JA, Kumar RB, Mikolaitis RA, Wilke CT, et al., editors. Disease-specific patient reported outcome tools for systemic lupus erythematosus. Seminars in arthritis and rheumatism; 2012: Elsevier.[2]Paasche-Orlow MK, Parker RM, Gazmararian JA, Nielsen-Bohlman LT, Rudd RR. The prevalence of limited health literacy. Journal of general internal medicine. 2005;20(2):175-84.Disclosure of Interests:None declared

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