How to Deal with Interval-Censored Data Practically while Assessing the Progression-Free Survival: A Step-by-Step Guide Using SAS and R Software.
We describe how to estimate progression-free survival while dealing with interval-censored data in the setting of clinical trials in oncology. Three procedures with SAS and R statistical software are described: one allowing for a nonparametric maximum likelihood estimation of the survival curve using the EM-ICM (Expectation and Maximization-Iterative Convex Minorant) algorithm as described by Wellner and Zhan in 1997; a sensitivity analysis procedure in which the progression time is assigned (i) at the midpoint, (ii) at the upper limit (reflecting the standard analysis when the progression time is assigned at the first radiologic exam showing progressive disease), or (iii) at the lower limit of the censoring interval; and finally, two multiple imputations are described considering a uniform or the nonparametric maximum likelihood estimation (NPMLE) distribution. Clin Cancer Res; 22(23); 5629-35. ©2016 AACR.
Department of Clinical Research, François Baclesse Cancer Center, Caen, France. audrey.dugue@baclesse.unicancer.fr.
Audrey Emmanuelle
AE
Dugué
Clinical and Epidemiological Research Unit, Bergonié Cancer Institute, Bordeaux, France.
Marina
M
Pulido
Biostatistics and Therapeutics Evaluation Unit, Léon Bérard Cancer Center, Lyon, France.
Sylvie
S
Chabaud
Department of Biostatistics, Curie Cancer Institute, Paris, France.
Lisa
L
Belin
Epidemiology and Biostatistics Unit, Antoine Lacassagne Cancer Center, Nice, France.
Jocelyn
J
Gal
D000465:Algorithms; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D018572:Disease-Free Survival; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D012984:Software / Q000706:statistics & numerical data*; D013223:Statistics as Topic / Q000379:methods*; D016019:Survival Analysis