title: Testing independence of bivariate interval-censored data using modified Kendall's tau statistic.
abstract: In this paper, we study a nonparametric procedure to test independence of bivariate interval censored data; for both current status data (case 1 interval-censored data) and case 2 interval-censored data. To do it, we propose a score-based modification of the Kendall's tau statistic for bivariate interval-censored data. Our modification defines the Kendall's tau statistic with expected numbers of concordant and disconcordant pairs of data. The performance of the modified approach is illustrated by simulation studies and application to the AIDS study. We compare our method to alternative approaches such as the two-stage estimation method by Sun et al. (Scandinavian Journal of Statistics, 2006) and the multiple imputation method by Betensky and Finkelstein (Statistics in Medicine, 1999b).
authors.affiliation: Department of Statistics, Seoul National University, Seoul, Korea.
authors.forename: Yuneung
authors.initials: Y
authors.lastname: Kim
authors.affiliation: Department of Statistics, Seoul National University, Seoul, Korea.
authors.forename: Johan
authors.initials: J
authors.lastname: Lim
authors.affiliation: Department of Mathematics and Statistics, University of Maryland, Baltimore County, MD 21250, USA.
authors.forename: DoHwan
authors.initials: D
authors.lastname: Park
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000097:blood / Q000652:urine / Q000821:virology; D056808:Biostatistics / Q000379:methods*; D003587:Cytomegalovirus / Q000502:physiology; D005243:Feces / Q000382:microbiology; D006801:Humans; D015999:Multivariate Analysis; D015269:Mycobacterium avium Complex / Q000502:physiology; D013183:Sputum / Q000502:physiology; D018709:Statistics, Nonparametric
keywords: Bivariate failure time data; Case 2 interval-censored data; Current status data; Kendall's tau; Testing independence