Recently, with the growth of statistical developments for competing risks analysis, some methods have been proposed to compute sample size in this context. These methods differ from a modelling approach: one is based on the Cox regression model for the cause-specific hazard, while another relies on the Fine and Gray regression model for the subdistribution hazard of a competing risk. In this work, we compare these approaches, derive a new sample size for comparing cumulative incidence functions when the hazards are not proportional (either cause-specific or subdistribution) and give practical advices to choose the approach best suited for the study question.
Description
Sample size calculations in the presence of competing risks. - PubMed - NCBI
%0 Journal Article
%1 Latouche:2007:Stat-Med:17955563
%A Latouche, A
%A Porcher, R
%D 2007
%J Stat Med
%K CompetingRisks Multi-stateModels SampleSize SurvivalAnalysis statistics
%N 30
%P 5370-5380
%R 10.1002/sim.3114
%T Sample size calculations in the presence of competing risks
%U https://www.ncbi.nlm.nih.gov/pubmed/17955563
%V 26
%X Recently, with the growth of statistical developments for competing risks analysis, some methods have been proposed to compute sample size in this context. These methods differ from a modelling approach: one is based on the Cox regression model for the cause-specific hazard, while another relies on the Fine and Gray regression model for the subdistribution hazard of a competing risk. In this work, we compare these approaches, derive a new sample size for comparing cumulative incidence functions when the hazards are not proportional (either cause-specific or subdistribution) and give practical advices to choose the approach best suited for the study question.
@article{Latouche:2007:Stat-Med:17955563,
abstract = {Recently, with the growth of statistical developments for competing risks analysis, some methods have been proposed to compute sample size in this context. These methods differ from a modelling approach: one is based on the Cox regression model for the cause-specific hazard, while another relies on the Fine and Gray regression model for the subdistribution hazard of a competing risk. In this work, we compare these approaches, derive a new sample size for comparing cumulative incidence functions when the hazards are not proportional (either cause-specific or subdistribution) and give practical advices to choose the approach best suited for the study question.},
added-at = {2018-09-20T20:37:32.000+0200},
author = {Latouche, A and Porcher, R},
biburl = {https://www.bibsonomy.org/bibtex/235a0dd7ff39d167c0c5109d82a6d3d4c/jkd},
description = {Sample size calculations in the presence of competing risks. - PubMed - NCBI},
doi = {10.1002/sim.3114},
interhash = {1c452152bcdad44aa6e6c02a4f4c8345},
intrahash = {35a0dd7ff39d167c0c5109d82a6d3d4c},
journal = {Stat Med},
keywords = {CompetingRisks Multi-stateModels SampleSize SurvivalAnalysis statistics},
month = dec,
number = 30,
pages = {5370-5380},
pmid = {17955563},
timestamp = {2018-09-21T09:14:01.000+0200},
title = {Sample size calculations in the presence of competing risks},
url = {https://www.ncbi.nlm.nih.gov/pubmed/17955563},
volume = 26,
year = 2007
}