One- and two-sample nonparametric inference procedures in the presence of a mixture of independent and dependent censoring.
In survival analysis, the event time T is often subject to dependent censorship. Without assuming a parametric model between the failure and censoring times, the parameter Theta of interest, for example, the survival function of T, is generally not identifiable. On the other hand, the collection Omega of all attainable values for Theta may be well defined. In this article, we present nonparametric inference procedures for Omega in the presence of a mixture of dependent and independent censoring variables. By varying the criteria of classifying censoring to the dependent or independent category, our proposals can be quite useful for the so-called sensitivity analysis of censored failure times. The case that the failure time is subject to possibly dependent interval censorship is also discussed in this article. The new proposals are illustrated with data from two clinical studies on HIV-related diseases.
Park
Yuhyun
Y
Department of Biostatistics, Harvard University, 677 Huntington Avenue, Boston, MA 02115, USA.
Tian
Lu
L
Wei
L J
LJ
D019380:Anti-HIV Agents / Q000627:therapeutic use; D001699:Biometry / Q000379:methods*; D016001:Confidence Intervals; D015658:HIV Infections / Q000150:complications / Q000188:drug therapy / Q000401:mortality; D006467:Hemophilia A / Q000150:complications; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D018709:Statistics, Nonparametric; D016019:Survival Analysis
D019380:Anti-HIV Agents