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

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.

Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values

“…The pseudo-value technique was first postulated by Andersen and his colleagues [ 18 , 19 ] in the context of multi-state survival models with right-censored data. Since then, it has been well studied in various disciplines of statistics including the interval-censored data [ 20 , 21 ], clustered data [ 22 , 23 ], and machine learning methods [ 24 , 25 ].…”

“…The pseudo-values can then be used as the response variable in a regression model with the covariates [ 29 ]. Several studies reported that the type I error is well controlled at a nominal level of 0.05 while maintaining a high statistical power under the quasi-likelihood generalized linear mixed model [ 30 ] and generalized estimating equations framework [ 23 , 31 ] for pseudo-value regression approach.…”

A pseudo-value regression approach for differential network analysis of co-expression data

“…Several studies [ 20 , 21 ] purported that the pseudo-value regression has advantages that the pseudo-values derived from an asymptotically linear and unbiased estimator are approximately independent and identically distributed with the same conditional expectation. Ahn and Logan [ 22 ] and Ahn and Mendolia [ 23 ] showed that their pseudo-value approaches controlled the type I error while maintaining high power with clustered survival data. With these benefits, we propose a regression modeling method that regresses the jackknife pseudo-values [ 24 ] derived from a measure of connectivity of genes in a network to estimate the effects of predictors.…”

A semiparametric modeling approach for analyzing clinical biomarkers restricted to limits of detection

“…In this article, we propose a semiparametric approach for modeling biomarkers subjected to LOD through a pseudovalue approach which is distribution-free and can uniformly handle single as well multiple LODs is the same data. The pseudo-values have been previously implemented in regression of complex quantities in time-to-event analysis [21][22][23][24][25] ; however, we extend this approach in modeling biomarkers below the LOD for the first time. We implement a semiparametric inference procedure for the biomarkers below the LOD by enabling a pseudo-value based estimation through a generalized estimating equation framework.…”