ArticleThe annals of applied statistics2016
PSEUDO-VALUE APPROACH FOR CONDITIONAL QUANTILE RESIDUAL LIFETIME ANALYSIS FOR CLUSTERED SURVIVAL AND COMPETING RISKS DATA WITH APPLICATIONS TO BONE MARROW TRANSPLANT DATA.
Article in The annals of applied statistics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Rforce: Random Forests for Composite Endpoints.Statistics in medicine · 2026Article
- Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values.BMC bioinformatics · 2024Article
- A pseudo-value regression approach for differential network analysis of co-expression data.BMC bioinformatics · 2023Article
- A semiparametric modeling approach for analyzing clinical biomarkers restricted to limits of detection.Pharmaceutical statistics · 2021Article
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2 authors.
Funding
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.
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