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Interpretation of correlations in clinical research

Overview of attention for article published in Postgraduate Medicine, September 2017
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  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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twitter
2 tweeters

Readers on

mendeley
35 Mendeley
Title
Interpretation of correlations in clinical research
Published in
Postgraduate Medicine, September 2017
DOI 10.1080/00325481.2017.1383820
Pubmed ID
Authors

Man Hung, Jerry Bounsanga, Maren Wright Voss

Abstract

Critically analyzing research is a key skill in evidence-based practice and requires knowledge of research methods, results interpretation, and applications, all of which rely on a foundation based in statistics. Evidence-based practice makes high demands on trained medical professionals to interpret an ever-expanding array of research evidence. As clinical training emphasizes medical care rather than statistics, it is useful to review the basics of statistical methods and what they mean for interpreting clinical studies. We reviewed the basic concepts of correlational associations, violations of normality, unobserved variable bias, sample size, and alpha inflation. The foundations of causal inference were discussed and sound statistical analyses were examined. We discuss four ways in which correlational analysis is misused, including causal inference overreach, over-reliance on significance, alpha inflation, and sample size bias. Recent published studies in the medical field provide evidence of causal assertion overreach drawn from correlational findings. The findings present a primer on the assumptions and nature of correlational methods of analysis and urge clinicians to exercise appropriate caution as they critically analyze the evidence before them and evaluate evidence that supports practice. Critically analyzing new evidence requires statistical knowledge in addition to clinical knowledge. Studies can overstate relationships, expressing causal assertions when only correlational evidence is available. Failure to account for the effect of sample size in the analyses tends to overstate the importance of predictive variables. It is important not to overemphasize the statistical significance without consideration of effect size and whether differences could be considered clinically meaningful.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 26%
Student > Postgraduate 5 14%
Student > Ph. D. Student 4 11%
Professor 3 9%
Other 2 6%
Other 7 20%
Unknown 5 14%
Readers by discipline Count As %
Medicine and Dentistry 9 26%
Nursing and Health Professions 6 17%
Sports and Recreations 3 9%
Engineering 2 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Other 6 17%
Unknown 7 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 July 2020.
All research outputs
#9,116,549
of 15,543,024 outputs
Outputs from Postgraduate Medicine
#562
of 1,015 outputs
Outputs of similar age
#139,924
of 278,170 outputs
Outputs of similar age from Postgraduate Medicine
#7
of 31 outputs
Altmetric has tracked 15,543,024 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,015 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 41st percentile – i.e., 41% 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 278,170 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.