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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Statistical modelling for recurrent events: an application to sports injuries
|
|---|---|
| Published in |
British Journal of Sports Medicine, August 2012
|
| DOI | 10.1136/bjsports-2011-090803 |
| Pubmed ID | |
| Authors |
Shahid Ullah, Tim J Gabbett, Caroline F Finch |
| Abstract |
BACKGROUND: Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. OBJECTIVE: This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. METHODS: Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. RESULTS: The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. CONCLUSIONS: Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. |
X Demographics
The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 5 | 19% |
| Spain | 4 | 15% |
| United Kingdom | 3 | 12% |
| Switzerland | 1 | 4% |
| United States | 1 | 4% |
| Poland | 1 | 4% |
| Canada | 1 | 4% |
| Luxembourg | 1 | 4% |
| South Africa | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 7 | 27% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 16 | 62% |
| Scientists | 7 | 27% |
| Practitioners (doctors, other healthcare professionals) | 2 | 8% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 240 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | <1% |
| Hungary | 1 | <1% |
| Netherlands | 1 | <1% |
| Australia | 1 | <1% |
| Brazil | 1 | <1% |
| Switzerland | 1 | <1% |
| Denmark | 1 | <1% |
| Qatar | 1 | <1% |
| Spain | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 229 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 47 | 20% |
| Student > Master | 35 | 15% |
| Researcher | 26 | 11% |
| Student > Bachelor | 25 | 10% |
| Other | 15 | 6% |
| Other | 46 | 19% |
| Unknown | 46 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Sports and Recreations | 58 | 24% |
| Medicine and Dentistry | 57 | 24% |
| Mathematics | 18 | 8% |
| Nursing and Health Professions | 12 | 5% |
| Social Sciences | 7 | 3% |
| Other | 32 | 13% |
| Unknown | 56 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 27 March 2017.
All research outputs
#2,130,768
of 23,925,854 outputs
Outputs from British Journal of Sports Medicine
#2,829
of 6,305 outputs
Outputs of similar age
#13,553
of 168,757 outputs
Outputs of similar age from British Journal of Sports Medicine
#38
of 88 outputs
Altmetric has tracked 23,925,854 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,305 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 65.3. This one has gotten more attention than average, scoring higher than 55% 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 168,757 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 88 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 57% of its contemporaries.