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

Background 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 comp...
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Autor*in:	Teixeira, Laetitia [verfasserIn] Rodrigues, Anabela Carvalho, Maria J Cabrita, António Mendonça, Denisa

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Cause-specific hazard model Competing risks Cumulative incidence function Peritoneal dialysis Subdistribution hazard model Survival analysis

Anmerkung:	© Teixeira et al.; licensee BioMed Central Ltd. 2013

Übergeordnetes Werk:	Enthalten in: BMC nephrology - London : BioMed Central, 2000, 14(2013), 1 vom: 24. Mai
Übergeordnetes Werk:	volume:14 ; year:2013 ; number:1 ; day:24 ; month:05

Links:	Volltext

DOI / URN:	10.1186/1471-2369-14-110

Katalog-ID:	SPR027509230

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520			\|a Background 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.
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allfields	10.1186/1471-2369-14-110 doi (DE-627)SPR027509230 (SPR)1471-2369-14-110-e DE-627 ger DE-627 rakwb eng Teixeira, Laetitia verfasserin aut Modelling competing risks in nephrology research: an example in peritoneal dialysis 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teixeira et al.; licensee BioMed Central Ltd. 2013 Background 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. Cause-specific hazard model (dpeaa)DE-He213 Competing risks (dpeaa)DE-He213 Cumulative incidence function (dpeaa)DE-He213 Peritoneal dialysis (dpeaa)DE-He213 Subdistribution hazard model (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Rodrigues, Anabela aut Carvalho, Maria J aut Cabrita, António aut Mendonça, Denisa aut Enthalten in BMC nephrology London : BioMed Central, 2000 14(2013), 1 vom: 24. Mai (DE-627)326643672 (DE-600)2041348-8 1471-2369 nnns volume:14 year:2013 number:1 day:24 month:05 https://dx.doi.org/10.1186/1471-2369-14-110 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 24 05
spelling	10.1186/1471-2369-14-110 doi (DE-627)SPR027509230 (SPR)1471-2369-14-110-e DE-627 ger DE-627 rakwb eng Teixeira, Laetitia verfasserin aut Modelling competing risks in nephrology research: an example in peritoneal dialysis 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teixeira et al.; licensee BioMed Central Ltd. 2013 Background 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. Cause-specific hazard model (dpeaa)DE-He213 Competing risks (dpeaa)DE-He213 Cumulative incidence function (dpeaa)DE-He213 Peritoneal dialysis (dpeaa)DE-He213 Subdistribution hazard model (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Rodrigues, Anabela aut Carvalho, Maria J aut Cabrita, António aut Mendonça, Denisa aut Enthalten in BMC nephrology London : BioMed Central, 2000 14(2013), 1 vom: 24. Mai (DE-627)326643672 (DE-600)2041348-8 1471-2369 nnns volume:14 year:2013 number:1 day:24 month:05 https://dx.doi.org/10.1186/1471-2369-14-110 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 24 05
allfields_unstemmed	10.1186/1471-2369-14-110 doi (DE-627)SPR027509230 (SPR)1471-2369-14-110-e DE-627 ger DE-627 rakwb eng Teixeira, Laetitia verfasserin aut Modelling competing risks in nephrology research: an example in peritoneal dialysis 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teixeira et al.; licensee BioMed Central Ltd. 2013 Background 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. Cause-specific hazard model (dpeaa)DE-He213 Competing risks (dpeaa)DE-He213 Cumulative incidence function (dpeaa)DE-He213 Peritoneal dialysis (dpeaa)DE-He213 Subdistribution hazard model (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Rodrigues, Anabela aut Carvalho, Maria J aut Cabrita, António aut Mendonça, Denisa aut Enthalten in BMC nephrology London : BioMed Central, 2000 14(2013), 1 vom: 24. Mai (DE-627)326643672 (DE-600)2041348-8 1471-2369 nnns volume:14 year:2013 number:1 day:24 month:05 https://dx.doi.org/10.1186/1471-2369-14-110 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 24 05
allfieldsGer	10.1186/1471-2369-14-110 doi (DE-627)SPR027509230 (SPR)1471-2369-14-110-e DE-627 ger DE-627 rakwb eng Teixeira, Laetitia verfasserin aut Modelling competing risks in nephrology research: an example in peritoneal dialysis 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teixeira et al.; licensee BioMed Central Ltd. 2013 Background 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. Cause-specific hazard model (dpeaa)DE-He213 Competing risks (dpeaa)DE-He213 Cumulative incidence function (dpeaa)DE-He213 Peritoneal dialysis (dpeaa)DE-He213 Subdistribution hazard model (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Rodrigues, Anabela aut Carvalho, Maria J aut Cabrita, António aut Mendonça, Denisa aut Enthalten in BMC nephrology London : BioMed Central, 2000 14(2013), 1 vom: 24. Mai (DE-627)326643672 (DE-600)2041348-8 1471-2369 nnns volume:14 year:2013 number:1 day:24 month:05 https://dx.doi.org/10.1186/1471-2369-14-110 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 24 05
allfieldsSound	10.1186/1471-2369-14-110 doi (DE-627)SPR027509230 (SPR)1471-2369-14-110-e DE-627 ger DE-627 rakwb eng Teixeira, Laetitia verfasserin aut Modelling competing risks in nephrology research: an example in peritoneal dialysis 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teixeira et al.; licensee BioMed Central Ltd. 2013 Background 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. Cause-specific hazard model (dpeaa)DE-He213 Competing risks (dpeaa)DE-He213 Cumulative incidence function (dpeaa)DE-He213 Peritoneal dialysis (dpeaa)DE-He213 Subdistribution hazard model (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Rodrigues, Anabela aut Carvalho, Maria J aut Cabrita, António aut Mendonça, Denisa aut Enthalten in BMC nephrology London : BioMed Central, 2000 14(2013), 1 vom: 24. Mai (DE-627)326643672 (DE-600)2041348-8 1471-2369 nnns volume:14 year:2013 number:1 day:24 month:05 https://dx.doi.org/10.1186/1471-2369-14-110 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 24 05
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source	Enthalten in BMC nephrology 14(2013), 1 vom: 24. Mai volume:14 year:2013 number:1 day:24 month:05
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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. 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author	Teixeira, Laetitia
spellingShingle	Teixeira, Laetitia misc Cause-specific hazard model misc Competing risks misc Cumulative incidence function misc Peritoneal dialysis misc Subdistribution hazard model misc Survival analysis Modelling competing risks in nephrology research: an example in peritoneal dialysis
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topic_title	Modelling competing risks in nephrology research: an example in peritoneal dialysis Cause-specific hazard model (dpeaa)DE-He213 Competing risks (dpeaa)DE-He213 Cumulative incidence function (dpeaa)DE-He213 Peritoneal dialysis (dpeaa)DE-He213 Subdistribution hazard model (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213
topic	misc Cause-specific hazard model misc Competing risks misc Cumulative incidence function misc Peritoneal dialysis misc Subdistribution hazard model misc Survival analysis
topic_unstemmed	misc Cause-specific hazard model misc Competing risks misc Cumulative incidence function misc Peritoneal dialysis misc Subdistribution hazard model misc Survival analysis
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title	Modelling competing risks in nephrology research: an example in peritoneal dialysis
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title_full	Modelling competing risks in nephrology research: an example in peritoneal dialysis
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author_browse	Teixeira, Laetitia Rodrigues, Anabela Carvalho, Maria J Cabrita, António Mendonça, Denisa
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title_sort	modelling competing risks in nephrology research: an example in peritoneal dialysis
title_auth	Modelling competing risks in nephrology research: an example in peritoneal dialysis
abstract	Background 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. © Teixeira et al.; licensee BioMed Central Ltd. 2013
abstractGer	Background 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. © Teixeira et al.; licensee BioMed Central Ltd. 2013
abstract_unstemmed	Background 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. © Teixeira et al.; licensee BioMed Central Ltd. 2013
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title_short	Modelling competing risks in nephrology research: an example in peritoneal dialysis
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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. 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Modelling competing risks in nephrology research: an example in peritoneal dialysis

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