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

Overview of attention for article published in BMC Nephrology, May 2013
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Title
Modelling competing risks in nephrology research: an example in peritoneal dialysis
Published in
BMC Nephrology, May 2013
DOI 10.1186/1471-2369-14-110
Pubmed ID
Authors

Laetitia Teixeira, Anabela Rodrigues, Maria J Carvalho, António Cabrita, Denisa Mendonça

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 3%
Unknown 36 97%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 5 14%
Student > Ph. D. Student 4 11%
Other 4 11%
Student > Bachelor 3 8%
Researcher 3 8%
Other 11 30%
Unknown 7 19%
Readers by discipline Count As %
Medicine and Dentistry 13 35%
Nursing and Health Professions 4 11%
Mathematics 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Business, Management and Accounting 1 3%
Other 4 11%
Unknown 10 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 August 2020.
All research outputs
#16,505,920
of 24,288,533 outputs
Outputs from BMC Nephrology
#1,577
of 2,630 outputs
Outputs of similar age
#124,280
of 198,545 outputs
Outputs of similar age from BMC Nephrology
#33
of 47 outputs
Altmetric has tracked 24,288,533 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,630 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 198,545 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.