mmm: An R package for analyzing multivariate longitudinal data with multivariate marginal models

Asar, Özgür; Ilk, Ozlem

doi:10.1016/j.cmpb.2013.07.022

Cited by 18 publications

(28 citation statements)

References 22 publications

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“…The final most parsimonious model was conducted by dropping, one by one, the non-significant variables guided by the corrected quasi likelihood under independence model criterion (lower values – better fit). In order to examine whether any relationships found with HoNOS total scores were strongly associated with particular individual HoNOS scale change, change in individual HoNOS scales over the years were analysed using multivariate marginal models (R package mmm), 18 again with link function identity and exchangeable correlation structure. Effect sizes ( d ) were calculated following Morris 19 using calculators by Lenhard & Lenhard 20 …”

Section: Methodsmentioning

confidence: 99%

Continuity of care and clinical outcomes in the community for people with severe mental illness

Macdonald

Adamis²,

Craig

et al. 2018

Br J Psychiatry

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BackgroundHigh continuity of care is prized by users of mental health services and lauded in health policy. It is especially important in long-term conditions like schizophrenia. However, it is not routinely measured, and therefore not often evaluated when service reorganisations take place. In addition, the impact of continuity of care on clinical outcomes is unclear.AimsWe set out to examine continuity of care in people with schizophrenia, and to relate this to demographic variables and clinical outcomes.MethodPseudoanonymised community data from 5552 individuals with schizophrenia presenting over 11 years were examined for changes in continuity of care using the numbers of community teams caring for them and the Modified Modified Continuity Index (MMCI). These and demographic variables were related to clinical outcomes measured with the Health of the Nation Outcome Scales (HoNOS). Data were analysed using generalised estimating equations and multivariate marginal models.ResultsThere was a significant decline in MMCI and significant worsening of HoNOS total scores over 11 years. Higher (worse) HoNOS scores were significantly and independently related to older age, later years and both lower MMCI and more teams caring for the individual in each year. Most HoNOS scales contributed to the higher total scores.ConclusionsThere is evidence of declining continuity of care in this 11-year study of people with schizophrenia, and of an independent effect of this on worse clinical outcomes. We suggest that this is related to reorganisation of services.Declaration of interestNone.

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Section: Methodsmentioning

confidence: 99%

Continuity of care and clinical outcomes in the community for people with severe mental illness

Macdonald

Adamis²,

Craig

et al. 2018

Br J Psychiatry

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“…We fitted the traditional model of [14,15] and compared the results with Model 1 by using the mmm package. We obtained identical results as expected.…”

Section: Methodsmentioning

confidence: 99%

“…[14] proposed models for binary data with GEE. [15] generalized the work of [14] for other response families rather than binomial and proposed the R [16] package mmm [17]. [18] considered likelihood based models with common predictor effects for continuous data by using model selection tools.…”

Section: Introductionmentioning

confidence: 99%

Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R

Asar

Ilk

2014

Computer Methods and Programs in Biomedicine

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Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.

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“…In the application section, we showed that the MLD model can be used for comparing theories of interest, without making unverifiable assumptions about underlying distributions. Asar and Ilk (2013) proposed marginal model with shared-parameter within the GEE method (Asar and Ilk, 2013). To compare with our MLD model, they use the five dimensional model where each response variable pertains to a unique dimension.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Multivariate Logistic Distance Model for the Analysis of Multiple Binary Responses

Worku

Rooij

2018

J Classif

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Abstract:We propose a Multivariate Logistic Distance (MLD) model for the analysis of multiple binary responses in the presence of predictors. The MLD model can be used to simultaneously assess the dimensional/factorial structure of the data and to study the effect of the predictor variables on each of the response variables. To enhance interpretation, the results of the proposed model can be graphically represented in a biplot, showing predictor variable axes, the categories of the response variables and the subjects' positions. The interpretation of the biplot uses a distance rule. The MLD model belongs to the family of marginal models for multivariate responses, as opposed to latent variable models and conditionally specified models. By setting the distance between the two categories of every response variable to be equal, the MLD model becomes equivalent to a marginal model for multivariate binary data estimated using a GEE method. In that case the MLD model can be fitted using existing statistical packages with a GEE procedure, e.g., the genmod procedure from SAS or the geepack package from R. Without the equality constraint, the MLD model is a general model which can be fitted by its own right. We applied the proposed model to empirical data to illustrate its advantages.

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mmm: An R package for analyzing multivariate longitudinal data with multivariate marginal models

Cited by 18 publications

References 22 publications

Continuity of care and clinical outcomes in the community for people with severe mental illness

Continuity of care and clinical outcomes in the community for people with severe mental illness

Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R

A Multivariate Logistic Distance Model for the Analysis of Multiple Binary Responses

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