The Annals of Applied Statistics

Pseudo-value approach for conditional quantile residual lifetime analysis for clustered survival and competing risks data with applications to bone marrow transplant data

Kwang Woo Ahn and Brent R. Logan

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 10, Number 2 (2016), 618-637.

Dates
Received: July 2015
Revised: March 2016
First available in Project Euclid: 22 July 2016

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1469199887

Digital Object Identifier
doi:10.1214/16-AOAS927

Mathematical Reviews number (MathSciNet)
MR3528354

Zentralblatt MATH identifier
06625663

Keywords
Pseudo-value residual lifetime clustered data

Citation

Ahn, Kwang Woo; Logan, Brent R. 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. 10 (2016), no. 2, 618--637. doi:10.1214/16-AOAS927. https://projecteuclid.org/euclid.aoas/1469199887


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