Testing independence of bivariate interval-censored data using modified Kendall's tau statistic.
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).
['Kim|Yuneung|Y|', 'Lim|Johan|J|', 'Park|DoHwan|D|']
D000163:Acquired Immunodeficiency Syndrome D056808:Biostatistics D003587:Cytomegalovirus D005243:Feces D006801:Humans D015999:Multivariate Analysis D015269:Mycobacterium avium Complex D013183:Sputum D018709:Statistics, Nonparametric
Testing independence Kendall's tau Current status data Case 2 interval-censored data Bivariate failure time data