Sample size calculations in the presence of competing risks.
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.
Latouche
A
A
Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, Université Paris 7, Paris, France. aurelien.latouche@uvsq.fr
Porcher
R
R
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
D000960:Hypolipidemic Agents