ArticleBMC medical research methodology2019
Risk-adjusted cUSUM control charts for shared frailty survival models with application to hip replacement outcomes: a study using the NJR dataset.
Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Monitoring time to event in registry data using CUSUMs based on relative survival models.Statistical methods in medical research · 2026Article
- Re-establishing control limits in statistical process control analyses: the stable shift algorithm.BMJ quality & safety · 2026Article
- CGR-CUSUM: a continuous time generalized rapid response cumulative sum chart.Biostatistics (Oxford, England) · 2023Article
- A double-Cox model for non-proportional hazards survival analysis with frailty.Statistics in medicine · 2023Article
- The effect of hormone replacement therapy on the survival of UK women: a retrospective cohort study 1984-2017.BJOG : an international journal of obstetrics and gynaecology · 2022Article
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3 authors.
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Abstract
backgroundContinuous monitoring of surgical outcomes after joint replacement is needed to detect which brands' components have a higher than expected failure rate and are therefore no longer recommended to be used in surgical practice. We developed a monitoring method based on cumulative sum (CUSUM) chart specifically for this application.
methodsOur method entails the use of the competing risks model with the Weibull and the Gompertz hazard functions adjusted for observed covariates to approximate the baseline time-to-revision and time-to-death distributions, respectively. The correlated shared frailty terms for competing risks, corresponding to the operating unit, are also included in the model. A bootstrap-based boundary adjustment is then required for risk-adjusted CUSUM charts to guarantee a given probability of the false alarm rates. We propose a method to evaluate the CUSUM scores and the adjusted boundary for a survival model with the shared frailty terms. We also introduce a unit performance quality score based on the posterior frailty distribution. This method is illustrated using the 2003-2012 hip replacement data from the UK National Joint Registry (NJR).
resultsWe found that the best model included the shared frailty for revision but not for death. This means that the competing risks of revision and death are independent in NJR data. Our method was superior to the standard NJR methodology. For one of the two monitored components, it produced alarms four years before the increased failure rate came to the attention of the UK regulatory authorities. The hazard ratios of revision across the units varied from 0.38 to 2.28.
conclusionsAn earlier detection of failure signal by our method in comparison to the standard method used by the NJR may be explained by proper risk-adjustment and the ability to accommodate time-dependent hazards. The continuous monitoring of hip replacement outcomes should include risk adjustment at both the individual and unit level.
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