ArticleBMC nephrology2013
Modelling competing risks in nephrology research: an example in peritoneal dialysis.
Article in BMC nephrology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- Dynamic mortality predictions from serum albumin in dialysis patients using robust joint models with competing risks.Scientific reports · 2025Article
- Estimating risk of encapsulating peritoneal sclerosis accounting for the competing risk of death.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2019Article
- Risk factors associated with outcomes of peritoneal dialysis in Taiwan: An analysis using a competing risk model.Medicine · 2019Observational
- Duration of veno-arterial extracorporeal life support (VA ECMO) and outcome: an analysis of the Extracorporeal Life Support Organization (ELSO) registry.Critical care (London, England) · 2017Article
- Fine and Gray competing risk regression model to study the cause-specific under-five child mortality in Bangladesh.BMC international health and human rights · 2017Article
- Technique failure in Korean incident peritoneal dialysis patients: a national population-based study.Kidney research and clinical practice · 2016Article
- Age-dependent association between sex and renal cell carcinoma mortality: a population-based analysis.Scientific reports · 2015Article
- Characteristics and survival of patients with end stage renal disease and spina bifida in the United States renal data system.The Journal of urology · 2015Article
- Epidemiological study of chronic kidney disease progression: a large-scale population-based cohort study.Medicine · 2015Article
- Serum sclerostin is an independent predictor of mortality in hemodialysis patients.BMC nephrology · 2014Article
- Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome.Diabetes & vascular disease researchArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModelling competing risks is an essential issue in Nephrology Research. In peritoneal dialysis studies, sometimes inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In this situation a competing risk analysis should be preferable. The objectives of this study are to describe the bias resulting from the application of standard survival analysis to estimate peritonitis-free patient survival and to provide alternative statistical approaches taking competing risks into account.
methodsThe sample comprises patients included in a university hospital peritoneal dialysis program between October 1985 and June 2011 (n = 449). Cumulative incidence function and competing risk regression models based on cause-specific and subdistribution hazards were discussed.
resultsThe probability of occurrence of the first peritonitis is wrongly overestimated using Kaplan-Meier method. The cause-specific hazard model showed that factors associated with shorter time to first peritonitis were age (≥55 years) and previous treatment (haemodialysis). Taking competing risks into account in the subdistribution hazard model, age remained significant while gender (female) but not previous treatment was identified as a factor associated with a higher probability of first peritonitis event.
conclusionsIn the presence of competing risks outcomes, Kaplan-Meier estimates are biased as they overestimated the probability of the occurrence of an event of interest. Methods which take competing risks into account provide unbiased estimates of cumulative incidence for each specific outcome experienced by patients. Multivariable regression models such as those based on cause-specific hazard and on subdistribution hazard should be used in this competing risk setting.
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