Nonparametric estimation in a cure model with random cure times.
Acute respiratory distress syndrome (ARDS) is a life-threatening acute condition that sometimes follows pneumonia or surgery. Patients who recover and leave the hospital are considered to have been cured at the time they leave the hospital. These data differ from typical data in which cure is a possibility: death times are not observed for patients who are cured and cure times are observed and vary among patients. Here we apply a competing risks model to these data and show it to be equivalent to a mixture model, the more common approach for cure data. Further, we derive an estimator for the variance of the cumulative incidence function from the competing risks model, and thus for the cure rate, based on elementary calculations. We compare our variance estimator to Gray's (1988, Annals of Statistics 16, 1140-1154) estimator, which is based on counting process theory. We find our estimator to be slightly more accurate in small samples. We apply these results to data from an ARDS clinical trial.
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. betensky@hsph.harvard.edu
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D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D012128:Respiratory Distress Syndrome / Q000401:mortality / Q000628:therapy; D016019:Survival Analysis