title: Sample size calculations in the presence of competing risks.
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.
authors.affiliation: Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, Université Paris 7, Paris, France. aurelien.latouche@uvsq.fr
authors.forename: A
authors.initials: A
authors.lastname: Latouche
authors.forename: R
authors.initials: R
authors.lastname: Porcher
mesh_terms: D001699:Biometry / Q000379:methods*; D002318:Cardiovascular Diseases / Q000401:mortality / Q000517:prevention & control; D002985:Clinical Protocols; D016000:Cluster Analysis; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D004311:Double-Blind Method; D006801:Humans; D000960:Hypolipidemic Agents / Q000627:therapeutic use; D015994:Incidence; D016013:Likelihood Functions; D009203:Myocardial Infarction / Q000401:mortality / Q000517:prevention & control; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data*; D012307:Risk Factors; D018401:Sample Size; D013997:Time Factors
chemical_list: D000960:Hypolipidemic Agents