ArticleBMC musculoskeletal disorders2026
Applied comparison of stepwise Cox and Cox-LASSO regression in selecting determinants of joint replacement: insights from the Geelong Osteoporosis Study.
Article in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundJoint replacement (JR) is influenced by multiple determinants. Identifying an appropriate variable selection method is essential for accurate and interpretable models in musculoskeletal epidemiology. Although penalized methods such as LASSO are increasingly recommended, stepwise regression remains common in applied research. We compared these approaches for predicting JR in a large musculoskeletal cohort.
methodsData from 2931 participants aged 20-96y from the Geelong Osteoporosis Study were analysed. Potential determinants included sociodemographic, lifestyle, body composition, and comorbidity variables. Stepwise Cox regression was implemented using the stepAIC() function in the 'MASS' package, and Cox-LASSO regression was performed using cv.glmnet() in the 'glmnet' package, using R version 4.3.1. Model performance and generalizability were assessed through independent test validation and 5-fold cross-validation. Bootstrap resampling was used to evaluate performance differences between models.
resultsOver a median follow-up of 16.7y (IQR:9.7-23.2), both models consistently identified age, spine bone mineral density, and prior fracture as key determinants of JR. Stepwise Cox regression using forward selection achieved slightly higher C-index scores across independent train-test splits and 5-fold cross-validation. All models demonstrated good discriminatory ability (C-index > 0.7). Bootstrap resampling confirmed that the performance difference was unlikely due to random variation (95%CI: 0.0004-0.0190).
conclusionsThis case study revealed the practical implications of variable selection in musculoskeletal epidemiology. Stepwise Cox regression may remain useful for moderate-sized, low-dimensional datasets, while penalized methods are better suited to larger, more complex data. These findings guide researchers and clinicians aiming to balance methodological rigor with practical utility in predicting JR.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.