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A Proportional Hazards Model for Multivariate Interval‐Censored Failure Time Data
Author(s) -
Goggins William B.,
Finkelstein Dianne M.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00940.x
Subject(s) - multivariate statistics , proportional hazards model , medicine , observational study , confidence interval , multivariate analysis , urine , statistics , mathematics
Summary. This paper focuses on the methodology developed for analyzing a multivariate interval‐censored data set from an AIDS observational study. A purpose of the study was to determine the natural history of the opportunistic infection cytomeglovirus (CMV) in an HIV‐infected individual. For this observational study, laboratory tests were performed at scheduled clinic visits to test for the presence of the CMV virus in the blood and in the urine (called CMV shedding in the blood and urine). The study investigators were interested in determining whether the stage of HIV disease at study entry was predictive of an increased risk for CMV shedding in either the blood or the urine. If all patients had made each clinic visit, the data would be multivariate grouped failure time data and published methods could be used. However, many patients missed several visits, and when they returned, their lab tests indicated a change in their blood and/or urine CMV shedding status, resulting in interval‐censored failure time data. This paper outlines a method for applying the proportional hazards model to the analysis of multivariate interval‐censored failure time data from a study of CMV in HIV‐infected patients.