Multivariate longitudinal data analysis with censored and intermittent missing responses.
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DOI: 10.1002/sim.7692
Abstract: The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements… read more here.
Keywords: longitudinal data; censored intermittent; analysis; intermittent missing ... See more keywords
Deep learning for the dynamic prediction of multivariate longitudinal and survival data
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DOI: 10.1002/sim.9392
Abstract: The joint model for longitudinal and survival data improves time‐to‐event predictions by including longitudinal outcome variables in addition to baseline covariates. However, in practice, joint models may be limited by parametric assumptions in both the… read more here.
Keywords: survival data; time event; longitudinal survival; survival ... See more keywords
Robust estimation for the correlation matrix of multivariate longitudinal data
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DOI: 10.1080/00949655.2020.1780234
Abstract: Modelling the covariance structure of multivariate longitudinal data is more challenging than its univariate counterpart, owing to the complex correlated structure among multiple responses. Furthermore, there are little methods focusing on the robustness of estimating… read more here.
Keywords: longitudinal data; matrix; correlation matrix; multivariate longitudinal ... See more keywords
Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions.
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DOI: 10.1111/biom.13113
Abstract: 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… read more here.
Keywords: longitudinal data; covariance matrix; correlation; multivariate longitudinal ... See more keywords
Design and analysis of two-phase studies with multivariate longitudinal data.
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DOI: 10.1111/biom.13616
Abstract: Two-phase studies are crucial when outcome and covariate data are available in a first phase sample (e.g., a cohort study), but costs associated with retrospective ascertainment of a novel exposure limit the size of the… read more here.
Keywords: longitudinal data; phase studies; analysis; phase ... See more keywords
Estimating correlation between multivariate longitudinal data in the presence of heterogeneity
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DOI: 10.1186/s12874-017-0398-1
Abstract: BackgroundEstimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time.… read more here.
Keywords: longitudinal data; heterogeneity; correlation multivariate; correlation ... See more keywords