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Modelling competing risks in nephrology research: an example in peritoneal dialysis

2013Journal article Open access
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dc.contributor.authorTeixeira, L.
dc.contributor.authorRodrigues, A.
dc.contributor.authorCarvalho, M.
dc.contributor.authorCabrita, A.
dc.contributor.authorMendonça, D.
dc.date.accessioned2014-01-06T13:53:29Z
dc.date.available2014-01-06T13:53:29Z
dc.date.issued2013
dc.description.abstractBACKGROUND: Modelling 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: In 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.por
dc.identifier.citationBMC Nephrol. 2013 May 24;14(1):110. [Epub ahead of print]por
dc.identifier.issn1471-2369
dc.identifier.urihttp://hdl.handle.net/10400.16/1550
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherBioMed Centralpor
dc.relation.publisherversionhttp://www.biomedcentral.com/1471-2369/14/110por
dc.subjectCause-specific hazard modelpor
dc.subjectCompeting riskspor
dc.subjectCumulative incidence functionpor
dc.subjectPeritoneal dialysispor
dc.subjectSubdistribution hazard modelpor
dc.subjectSurvival analysispor
dc.titleModelling competing risks in nephrology research: an example in peritoneal dialysispor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceLondonpor
oaire.citation.titleBMC nephrologypor
rcaap.rightsopenAccesspor
rcaap.typearticlepor

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