The analysis of multivariate longitudinal data: A review

Verbeke, Geert; Fieuws, Steffen; Molenberghs, Geert; Davidian, Marie

doi:10.1177/0962280214539862

Cited by 73 publications

(105 citation statements)

References 35 publications

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“…The typical repeated measurement or longitudinal data analytical methods are linear mixed effect models (LMMs) or GEE in addition to repeated ANOVA (treat time as a factor) and hierarchical linear models [20][21][22][23][24][25]. Different types of models can be distinguished depending on whether the latent variables are assumed for the time dimension (e.g., ANOVA with time as a factor) and/or for the outcome repeated measurement dimension, and whether it measures and assesses how the association between the various outcomes evolves over time [21].…”

Section: Modeling For Multi-level Hospital Survey Data With Longitudimentioning

confidence: 99%

“…Different types of models can be distinguished depending on whether the latent variables are assumed for the time dimension (e.g., ANOVA with time as a factor) and/or for the outcome repeated measurement dimension, and whether it measures and assesses how the association between the various outcomes evolves over time [21]. The use of latent variables allows for more flexible data structures but also has important implications with respect to the interpretation of the various model parameters in order to understand the association between the evolutions of all outcomes.…”

Section: Modeling For Multi-level Hospital Survey Data With Longitudimentioning

confidence: 99%

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Comparisons of Modeling Approaches for Evaluating the Longitudinal Association in a Clustered Healthcare Intervention Study

Liang¹

2017

J Biom Biostat

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This paper addresses methodology issues related to evidence-based healthcare research, specifically when evaluating and analyzing the hospital practice environments (HPE) impacts on the patient health outcomes are conducted in longitudinal intervention survey studies. HPE include the spatially clustered hospital characteristics, including practice environment scale (PES) measures, hospital facilities, nursing staffing and nursing attributes. The longitudinal associations between HPE and patient smoking cessation counseling (SCC) activities, and patient heart failure (HF) outcomes are examined. Various longitudinal and hierarchical modeling are compared including linear mixed models with restricted maximum likelihood estimation, generalized estimating equations with quasi-likelihood estimation, hierarchical linear regression models with nonparametric generalized least squares estimations, and repeated ANOVA. Moreover, both pre-modeling including the items/dimension reduction issues for longitudinal item-response hospital survey data and post-modeling (the mediation analysis) are discussed and conducted. Results show some methodology and solution differences when including the spatial or temporal correlations of HPE simultaneously for examining the longitudinal effects of HPE on HF core outcome measures adjusted or potentially mediated by SCC and nurse staffing environmental variables. This may have implications and potential impact for healthcare decision-making. Patients can benefit from these research findings.

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Section: Modeling For Multi-level Hospital Survey Data With Longitudimentioning

confidence: 99%

Section: Modeling For Multi-level Hospital Survey Data With Longitudimentioning

confidence: 99%

Comparisons of Modeling Approaches for Evaluating the Longitudinal Association in a Clustered Healthcare Intervention Study

Liang¹

2017

J Biom Biostat

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“…By specifying an unstructured cross outcome random effect covariance matrix, the mixed effects model can robustly model a correlation structure that differs across outcome. The challenges in using a random effects structure in a multivariate longitudinal context are discussed in detail by Fieuws and Verbeke [11] and Verbeke et al [4]. To reduce computational burden of the multivariate random effects model, Fieuws and Verbeke [11] propose an estimation approach where pairwise bivariate models are fit for each pair of outcomes, and parameter estimates are averaged across the pairwise models in order to make inference on parameters in the multivariate model.…”

Section: A Mixed Model For Rates Of Changementioning

confidence: 99%

“…When inference on each outcome is of primary interest, it is often beneficial to allow for separate structures for each outcome because a uniform longitudinal and multivariate structure may be difficult to justify. Further discussion comparing latent variable models with other multivariate longitudinal methods in a more general setting has been made by Verbeke et al [4].…”

Section: Introductionmentioning

confidence: 99%

Multivariate analysis of longitudinal rates of change

Bryan

Heagerty

2016

Statistics in Medicine

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Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants.

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“…A variety of models has been proposed in the statistical literature, and a general overview of these models for multivariate longitudinal data can be found in Verbeke et al (2014). Several studies have proposed models for the covariance matrix of multivariate longitudinal data.…”

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confidence: 99%

Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions

et al. 2019

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Linear models are typically used to analyze multivariate longitudinal data. With these models, estimating the covariance matrix is not easy because the covariance matrix should account for complex correlated structures: the correlation between responses at each time point, the correlation within separate responses over time, and the cross‐correlation between different responses at different times. In addition, the estimated covariance matrix should satisfy the positive definiteness condition, and it may be heteroscedastic. However, in practice, the structure of the covariance matrix is assumed to be homoscedastic and highly parsimonious, such as exchangeable or autoregressive with order one. These assumptions are too strong and result in inefficient estimates of the effects of covariates. Several studies have been conducted to solve these restrictions using modified Cholesky decomposition (MCD) and linear covariance models. However, modeling the correlation between responses at each time point is not easy because there is no natural ordering of the responses. In this paper, we use MCD and hypersphere decomposition to model the complex correlation structures for multivariate longitudinal data. We observe that the estimated covariance matrix using the decompositions is positive‐definite and can be heteroscedastic and that it is also interpretable. The proposed methods are illustrated using data from a nonalcoholic fatty liver disease study.

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The analysis of multivariate longitudinal data: A review

Cited by 73 publications

References 35 publications

Comparisons of Modeling Approaches for Evaluating the Longitudinal Association in a Clustered Healthcare Intervention Study

Comparisons of Modeling Approaches for Evaluating the Longitudinal Association in a Clustered Healthcare Intervention Study

Multivariate analysis of longitudinal rates of change

Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions

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