Medical College of Wisconsin
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PSEUDO-VALUE APPROACH FOR CONDITIONAL QUANTILE RESIDUAL LIFETIME ANALYSIS FOR CLUSTERED SURVIVAL AND COMPETING RISKS DATA WITH APPLICATIONS TO BONE MARROW TRANSPLANT DATA. Ann Appl Stat 2016 Jun;10(2):618-637 PMID: 29081872 PMCID: PMC5656291

Pubmed ID

29081872

DOI

10.1214/16-AOAS927

Abstract

Quantile residual lifetime analysis is conducted to compare remaining lifetimes among groups for survival data. Evaluating residual lifetimes among groups after adjustment for covariates is often of interest. The current literature is limited to comparing two groups for independent data. We propose a pseudo-value approach to compare quantile residual lifetimes given covariates between multiple groups for independent and clustered survival data. The proposed method considers clustered event times and clustered censoring times in addition to independent event times and censoring times. We show that the method can also be used to compare multiple groups on the cause specific residual life distribution in the competing risk setting, for which there are no current methods which account for clustering. The empirical Type I errors and statistical power of the proposed study are examined in a simulation study, which shows that the proposed method controls Type I errors very well and has higher power than an existing method. The proposed method is illustrated by a bone marrow transplant data set.

Author List

Ahn KW, Logan BR

Authors

Kwang Woo Ahn PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Brent R. Logan PhD Director, Professor in the Institute for Health and Equity department at Medical College of Wisconsin




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