Estimating a survival curve with unlinked entry and failure times.
In monitoring a clinical trial or other observational study with a survival endpoint, sometimes the numbers of patients entering and dying at each time point are presented, but the connections between them are kept confidential. Hence, the exact time to failure or censoring for each individual is missing. We refer to such a study monitoring table with missing pairing information between the entry and death times as a 'broken' survival data set. In this paper we study the problem of estimating the survival distribution from a broken survival data set. We have developed two methods, likelihood-based estimation and self-consistency estimation, to estimate the survival curve parametrically and empirically, respectively. We use simulations to study the properties of these methods, and illustrate them with data from the STELLAR-3 trial.
Wu
Yujun
Y
Department of Biostatistics, School of Public Health, and Division of Biometrics, The Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey, New Brunswick, NJ 08901, USA. wuy5@umdnj.edu
Shih
Weichung J
WJ
Moore
Dirk F
DF
D000465:Algorithms; D015982:Bias; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D016019:Survival Analysis; D014481:United States