mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses.
CHICAS, Lancaster Medical School, Faculty of Health and Medicine, Lancaster LA1 4YG, UK. Electronic address: o.asar@lancaster.ac.uk.
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D000465:Algorithms; D008137:Longitudinal Studies; D008962:Models, Theoretical; D015999:Multivariate Analysis
Correlated data; Medical studies; Multiple outcomes; Package presentation; Population-averaged inference; Statistical software