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A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.

Overview of attention for article published in The Annals of Applied Statistics, January 2024
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
11 X users

Readers on

mendeley
8 Mendeley
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Title
A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.
Published in
The Annals of Applied Statistics, January 2024
DOI 10.1214/23-aoas1792
Pubmed ID
Authors

Xinyuan Chen, Michael O Harhay, Guangyu Tong, Fan Li

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Other 2 25%
Student > Bachelor 1 13%
Student > Doctoral Student 1 13%
Unknown 2 25%
Readers by discipline Count As %
Medicine and Dentistry 3 38%
Nursing and Health Professions 2 25%
Economics, Econometrics and Finance 1 13%
Unknown 2 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 13 February 2024.
All research outputs
#5,594,920
of 26,420,475 outputs
Outputs from The Annals of Applied Statistics
#137
of 722 outputs
Outputs of similar age
#88,722
of 380,462 outputs
Outputs of similar age from The Annals of Applied Statistics
#2
of 11 outputs
Altmetric has tracked 26,420,475 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 722 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 81% 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 380,462 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 76% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.