Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference
Open accessAbstract
Objective: Compare performance between an injury prediction model categorising predictors and one that did not and compare a selection of predictors based on univariate significance versus assessing non-linear relationships. Methods: Validation and replication of a previously developed injury prediction model in a cohort of 1466 service members followed for 1 year after physical performance, medical history and sociodemographic variables were collected. The original model dichotomised 11 predictors. The second model (M2) kept predictors continuous but assumed linearity and the third model (M3) conducted non-linear transformations. The fourth model (M4) chose predictors the proper way (clinical reasoning and supporting evidence). Model performance was assessed with R 2 , calibration in the large, calibration slope and discrimination. Decision curve analyses were performed with risk thresholds from 0.25 to 0.50. Results: 478 personnel sustained an injury. The original model demonstrated poorer R 2 (original:0.07; M2:0.63; M3:0.64; M4:0.08), calibration in the large (original:−0.11 (95% CI −0.22 to 0.00); M2: −0.02 (95% CI −0.17 to 0.13); M3:0.03 (95% CI −0.13 to 0.19); M4: −0.13 (95% CI −0.25 to –0.01)), calibration slope (original:0.84 (95% CI 0.61 to 1.07); M2:0.97 (95% CI 0.86 to 1.08); M3:0.90 (95% CI 0.75 to 1.05); M4: 081 (95% CI 0.59 to 1.03) and discrimination (original:0.63 (95% CI 0.60 to 0.66); M2:0.90 (95% CI 0.88 to 0.92); M3:0.90 (95% CI 0.88 to 0.92); M4: 0.63 (95% CI 0.60 to 0.66)). At 0.25 injury risk, M2 and M3 demonstrated a 0.43 net benefit improvement. At 0.50 injury risk, M2 and M3 demonstrated a 0.33 net benefit improvement compared with the original model. Conclusion: Model performance was substantially worse in the models with dichotomised variables. This highlights the need to follow established recommendations when developing prediction models.
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Dichotomising or categorising continuous predictors reduces information and diminishes statistical power.
“Dichotomising or categorising continuous predictors reduces information and diminishes statistical power and should be avoided.41 42”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [41]
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Self-citation
- Authors
- Rhon, D. I., Teyhen, D. S., Collins, G. S., & Bullock, G. S.
- Affiliations
- Brooke Army Medical Center (US)
- United States Army (US)
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, Oxford University, Oxford, UK
- University of Oxford (GB)
- Corresponding author
- Rhon, Daniel I
- Venue
- BMJ Open Sport & Exercise Medicine
- Publisher
- BMJ
- Published
- 2022-10
- Volume
- 8
- Issue
- 4
- Pages
- e001388
- ISSN
- 2055-7647 (ISSN-L)
- PMC
- PMC9577931
- OpenAlex
- W4306253192
- Cited by
- 20
- References
- 39
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 5 · 2023: 3
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Open Sport & Exercise Medicine (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- Funders
- Uniformed Services University of the Health Sciences (MIRROR HU00011920011)
- Congressionally Directed Medical Research Programs ((D10_I_AR_J5_951))
- Topics
- Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Performance
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports injuries and prevention
- Keywords
- musculoskeletal injuries, injury prediction, prediction model performance, nonlinear relationship, continuous predictors, model calibration, model discrimination, decision curve analysis, military personnel, physical performance, predictor selection, risk thresholds, injury prevention
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