Analysis of failure time data with dependent interval censoring.
This article develops a method for the analysis of screening data for which the chance of being screened is dependent on the event of interest (informative censoring). Because not all subjects make all screening visits, the data on the failure of interest is interval censored. We propose a model that will properly adjust for the dependence to obtain an unbiased estimate of the nonparametric failure time function, and we provide an extension for applying the method for estimation of the regression parameters from a (discrete time) proportional hazards regression model. The method is applied on a data set from an observational study of cytomegalovirus shedding in a population of HIV-infected subjects who participated in a trial conducted by the AIDS Clinical Trials Group.
Finkelstein
Dianne M
DM
Biostatistics Center, Massachusetts General Hospital, Boston 02114, USA. finkel@biostat.harvard.edu
Goggins
William B
WB
Schoenfeld
David A
DA
D017088:AIDS-Related Opportunistic Infections / Q000821:virology; D000465:Algorithms; D015982:Bias; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003587:Cytomegalovirus / Q000302:isolation & purification; D003586:Cytomegalovirus Infections / Q000150:complications / Q000821:virology; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models