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Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly

Overview of attention for article published in Clinical Epidemiology, December 2016
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Title
Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly
Published in
Clinical Epidemiology, December 2016
DOI 10.2147/clep.s121023
Pubmed ID
Authors

Lidia MVR Moura, M Brandon Westover, David Kwasnik, Andrew J Cole, John Hsu

Abstract

The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer's disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 16%
Professor > Associate Professor 6 13%
Student > Master 5 11%
Student > Ph. D. Student 3 7%
Researcher 3 7%
Other 8 18%
Unknown 13 29%
Readers by discipline Count As %
Medicine and Dentistry 14 31%
Neuroscience 5 11%
Computer Science 2 4%
Mathematics 1 2%
Nursing and Health Professions 1 2%
Other 5 11%
Unknown 17 38%
Attention Score in Context

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 03 January 2017.
All research outputs
#17,285,036
of 25,371,288 outputs
Outputs from Clinical Epidemiology
#530
of 793 outputs
Outputs of similar age
#261,646
of 416,429 outputs
Outputs of similar age from Clinical Epidemiology
#4
of 6 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 793 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.5. 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 416,429 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
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