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joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

Overview of attention for article published in BMC Medical Research Methodology, June 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)

Mentioned by

blogs
1 blog
twitter
10 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
36 Mendeley
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Title
joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes
Published in
BMC Medical Research Methodology, June 2018
DOI 10.1186/s12874-018-0502-1
Pubmed ID
Authors

Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, Ruwanthi Kolamunnage-Dona

Abstract

Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome. Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a practical algorithm for fitting the models, and demonstrate how to fit the models using a new package for the statistical software platform R, joineRML. A multivariate linear mixed sub-model is specified for the longitudinal outcomes, and a Cox proportional hazards regression model with time-varying covariates is specified for the event time sub-model. The association between models is captured through a zero-mean multivariate latent Gaussian process. The models are fitted using a Monte Carlo Expectation-Maximisation algorithm, and inferences are based on approximate standard errors from the empirical profile information matrix, which are contrasted to an alternative bootstrap estimation approach. We illustrate the model and software on a real data example for patients with primary biliary cirrhosis with three repeatedly measured biomarkers. An open-source software package capable of fitting multivariate joint models is available. The underlying algorithm and source code makes use of several methods to increase computational speed.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 25%
Student > Ph. D. Student 7 19%
Student > Master 4 11%
Student > Doctoral Student 3 8%
Professor 2 6%
Other 3 8%
Unknown 8 22%
Readers by discipline Count As %
Mathematics 11 31%
Medicine and Dentistry 4 11%
Chemistry 2 6%
Computer Science 2 6%
Psychology 1 3%
Other 4 11%
Unknown 12 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 January 2019.
All research outputs
#1,317,593
of 14,207,392 outputs
Outputs from BMC Medical Research Methodology
#220
of 1,304 outputs
Outputs of similar age
#43,060
of 276,246 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 2 outputs
Altmetric has tracked 14,207,392 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,304 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 276,246 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them