Evidence map›Paper›PMID 36593432›Full record

ArticleLifetime data analysis2023

A series of two-sample non-parametric tests for quantile residual life time.

Yimeng Liu, Liwen Wu, Gong Tang, Abdus S Wahed

Abstract readMulticenter Study
In one paragraph

Article in Lifetime data analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yimeng LiuDepartment of Biostatistics, University of Pittsburgh, 130 Desoto street, Pittsburgh, PA, 15261, USA. cn.yimeng@gmail.com.ORCID 0000-0002-3688-7876
Liwen WuDepartment of Biostatistics, University of Pittsburgh, 130 Desoto street, Pittsburgh, PA, 15261, USA.
Gong TangDepartment of Biostatistics, University of Pittsburgh, 130 Desoto street, Pittsburgh, PA, 15261, USA.
Abdus S WahedDepartment of Biostatistics, University of Pittsburgh, 130 Desoto street, Pittsburgh, PA, 15261, USA.

Funding

Statistics CoreU10CA180822 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI James J. Dignam, GREG YOTHERS · 2014 to 2026
$146.4M
NCI NIH HHS U10 CA180822NCI NIH HHS U10-CA180822
6 · The paper itself

Abstract

Quantile residual lifetime (QRL) is of significant interest in many clinical studies as an easily interpretable quantity compared to other summary measures of survival distributions. In cancer or other chronic diseases, treatments are often compared based on the distributions or quantiles of the residual lifetime. Thus a common problem of interest is to test the equality of the QRL between two populations. In this paper, we propose two classes of tests to compare two QRLs; one class is based on the difference between two estimated QRLs, and the other is based on the estimating function of the QRL, where the estimated QRL from one sample is plugged into the QRL-estimating-function of the other sample. We outline the asymptotic properties of these test statistics. Simulation studies demonstrate that the proposed tests produced Type I errors closer to the nominal level and are superior to some existing tests based on both Type I error and power. Our proposed test statistics are also computationally less intensive and more straightforward compared to tests based on the confidence intervals. We applied the proposed methods to a randomized multicenter phase III trial for breast cancer patients.

Indexed as

Breast NeoplasmsComputer SimulationFemaleHumansSurvival AnalysisInverse probability weightingKaplan–MeierKernel density estimatorsQuantile residual lifetimeResidual lifetimeWald test

Identifiers

PMID36593432
PMCPMC12054101

What OpenQuestion holds

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Registered trials

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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.