↓ Skip to main content

Statistical Issues in TBI Clinical Studies

Overview of attention for article published in Frontiers in Neurology, January 2013
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
47 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Statistical Issues in TBI Clinical Studies
Published in
Frontiers in Neurology, January 2013
DOI 10.3389/fneur.2013.00177
Pubmed ID
Authors

Paul E. Rapp, Christopher J. Cellucci, David O. Keyser, Adele M. K. Gilpin, David M. Darmon

Abstract

The identification and longitudinal assessment of traumatic brain injury presents several challenges. Because these injuries can have subtle effects, efforts to find quantitative physiological measures that can be used to characterize traumatic brain injury are receiving increased attention. The results of this research must be considered with care. Six reasons for cautious assessment are outlined in this paper. None of the issues raised here are new. They are standard elements in the technical literature that describes the mathematical analysis of clinical data. The purpose of this paper is to draw attention to these issues because they need to be considered when clinicians evaluate the usefulness of this research. In some instances these points are demonstrated by simulation studies of diagnostic processes. We take as an additional objective the explicit presentation of the mathematical methods used to reach these conclusions. This material is in the appendices. The following points are made: (1) A statistically significant separation of a clinical population from a control population does not ensure a successful diagnostic procedure. (2) Adding more variables to a diagnostic discrimination can, in some instances, actually reduce classification accuracy. (3) A high sensitivity and specificity in a TBI versus control population classification does not ensure diagnostic successes when the method is applied in a more general neuropsychiatric population. (4) Evaluation of treatment effectiveness must recognize that high variability is a pronounced characteristic of an injured central nervous system and that results can be confounded by either disease progression or spontaneous recovery. A large pre-treatment versus post-treatment effect size does not, of itself, establish a successful treatment. (5) A procedure for discriminating between treatment responders and non-responders requires, minimally, a two phase investigation. This procedure must include a mechanism to discriminate between treatment responders, placebo responders, and spontaneous recovery. (6) A search for prodromes of neuropsychiatric disorders following traumatic brain injury can be implemented with these procedures.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 6%
Spain 1 2%
Unknown 43 91%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 8 17%
Researcher 6 13%
Professor 5 11%
Student > Master 5 11%
Student > Ph. D. Student 4 9%
Other 9 19%
Unknown 10 21%
Readers by discipline Count As %
Medicine and Dentistry 11 23%
Psychology 7 15%
Neuroscience 6 13%
Nursing and Health Professions 2 4%
Agricultural and Biological Sciences 2 4%
Other 6 13%
Unknown 13 28%
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 19 November 2013.
All research outputs
#20,210,424
of 22,731,677 outputs
Outputs from Frontiers in Neurology
#8,642
of 11,635 outputs
Outputs of similar age
#248,807
of 280,774 outputs
Outputs of similar age from Frontiers in Neurology
#117
of 210 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,635 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 1st percentile – i.e., 1% 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 280,774 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.