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A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements

Overview of attention for article published in Statistical Methods in Medical Research, November 2015
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  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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1 tweeter

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Title
A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements
Published in
Statistical Methods in Medical Research, November 2015
DOI 10.1177/0962280215615003
Pubmed ID
Authors

Zeynep Kalaylioglu, Haydar Demirhan

Abstract

Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 43%
Student > Ph. D. Student 2 29%
Lecturer 1 14%
Researcher 1 14%
Readers by discipline Count As %
Mathematics 3 43%
Medicine and Dentistry 2 29%
Unspecified 1 14%
Environmental Science 1 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 November 2015.
All research outputs
#9,460,763
of 11,842,921 outputs
Outputs from Statistical Methods in Medical Research
#357
of 663 outputs
Outputs of similar age
#173,856
of 252,733 outputs
Outputs of similar age from Statistical Methods in Medical Research
#6
of 20 outputs
Altmetric has tracked 11,842,921 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 663 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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 252,733 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.