ArticleStatistics in medicine2014
Pseudo-value approach for comparing survival medians for dependent data.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks.Arabian journal of mathematics · 2025Article
- 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 Pseudo-Value Approach to Analyze the Semantic Similarity of the Speech of Children With and Without Autism Spectrum Disorder.Frontiers in psychology · 2021Article
- PSEUDO-VALUE APPROACH FOR CONDITIONAL QUANTILE RESIDUAL LIFETIME ANALYSIS FOR CLUSTERED SURVIVAL AND COMPETING RISKS DATA WITH APPLICATIONS TO BONE MARROW TRANSPLANT DATA.The annals of applied statistics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Survival median is commonly used to compare treatment groups in cancer-related research. The current literature focuses on developing tests for independent survival data. However, researchers often encounter dependent survival data such as matched pair data or clustered data. We propose a pseudo-value approach to test the equality of survival medians for both independent and dependent survival data. We investigate the type I error and power of the proposed method by a simulation study, in which we examine independent and dependent data. The simulation study shows that the proposed method performs equivalently to the existing methods for independent survival data and performs better for dependent survival data. A study comparing survival median times for bone marrow transplants illustrates the proposed method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.