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Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old
JBMR plus, 2023-08, Vol.7 (8), p.e10757-n/a
Langsetmo, Lisa
Schousboe, John T.
Taylor, Brent C.
Cauley, Jane A.
Fink, Howard A.
Cawthon, Peggy M.
Kado, Deborah M.
Ensrud, Kristine E.
2023
Details
Autor(en) / Beteiligte
Langsetmo, Lisa
Schousboe, John T.
Taylor, Brent C.
Cauley, Jane A.
Fink, Howard A.
Cawthon, Peggy M.
Kado, Deborah M.
Ensrud, Kristine E.
Titel
Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old
Ist Teil von
JBMR plus, 2023-08, Vol.7 (8), p.e10757-n/a
Ort / Verlag
Hoboken, USA: John Wiley & Sons, Inc
Erscheinungsjahr
2023
Link zum Volltext
Quelle
Wiley Online Library Journals
Beschreibungen/Notizen
Targeted fracture prevention strategies among late‐life adults should balance fracture risk versus competing mortality risk. Models have previously been constructed using Fine‐Gray subdistribution methods. We used a machine learning method adapted for competing risk survival time to evaluate candidate risk factors and create models for hip fractures and competing mortality among men and women aged 80 years and older using data from three prospective cohorts (Study of Osteoporotic Fractures [SOF], Osteoporotic Fracture in Men study [MrOS], Health Aging and Body Composition study [HABC]). Random forest competing risk models were used to estimate absolute 5‐year risk of hip fracture and absolute 5‐year risk of competing mortality (excluding post–hip fracture deaths). Models were constructed for both outcomes simultaneously; minimal depth was used to rank and select variables for smaller models. Outcome specific models were constructed; variable importance was used to rank and select variables for inclusion in smaller random forest models. Random forest models were compared to simple Fine‐Gray models with six variables selected a priori. Top variables for competing risk random forests were frailty and related components in men while top variables were age, bone mineral density (BMD) (total hip, femoral neck), and frailty components in women. In both men and women, outcome specific rankings strongly favored BMD variables for hip fracture prediction while frailty and components were strongly associated with competing mortality. Model discrimination for random forest models varied from 0.65 for mortality in women to 0.81 for hip fracture in men and depended on model choice and variables included. Random models performed slightly better than simple Fine‐Gray model for prediction of competing mortality, but similarly for prediction of hip fractures. Random forests can be used to estimate risk of hip fracture and competing mortality among the oldest old. Modest gains in performance for mortality without hip fracture compared to Fine‐Gray models must be weighed against increased complexity. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Sprache
Englisch
Identifikatoren
ISSN: 2473-4039
eISSN: 2473-4039
DOI: 10.1002/jbm4.10757
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_edb88b59b52d4a75b697e2382c6450cb
Format
–
Schlagworte
Age
,
Aging
,
Body composition
,
Bone mineral density
,
Cancer
,
Cardiovascular disease
,
Dementia
,
Diabetes
,
Disease prevention
,
Diuretics
,
Fractures
,
Hip
,
HIP FRACTURE
,
Hip joint
,
Hormone replacement therapy
,
MACHINE LEARNING
,
Mortality
,
Older people
,
Osteoporosis
,
Parkinson's disease
,
Predictions
,
RANDOM FOREST
,
Risk factors
,
Self report
,
Variables
,
Womens health
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