An extension of Kendall's coefficient of concordance to bivariate interval censored data.
Non-parametric tests of independence, as well as accompanying measures of association, are essential tools for the analysis of bivariate data. Such tests and measures have been developed for uncensored and right censored failure time data, but have not been developed for interval censored failure time data. Bivariate interval censored data arise in AIDS studies in which screening tests for early signs of viral and bacterial infection are done at clinic visits. Because of missed clinic visits, the actual times of first positive screening tests are interval censored. To handle such data, we propose an extension of Kendall's coefficient of concordance. We apply it to data from an AIDS study that recorded times of shedding of cytomegalovirus (CMV) and times of colonization of mycobacterium avium complex (MAC). We examine the performance of our proposed measure through a simulation study.
Betensky
R A
RA
Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA. betensky@hsph.harvard.edu
Finkelstein
D M
DM
D000163:Acquired Immunodeficiency Syndrome / Q000150:complications; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003587:Cytomegalovirus / Q000302:isolation & purification; D003586:Cytomegalovirus Infections / Q000097:blood / Q000150:complications / Q000652:urine; D005243:Feces / Q000382:microbiology; D006801:Humans; D015269:Mycobacterium avium Complex / Q000302:isolation & purification; D015270:Mycobacterium avium-intracellulare Infection / Q000150:complications; D013183:Sputum / Q000382:microbiology; D018709:Statistics, Nonparametric