Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling.

Abstract read

In one paragraph

Article in Lifetime data analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Pseudo-Observation Approach for Length-Biased Cox Proportional Hazards Model.Biometrical journal. Biometrische Zeitschrift · 2025
Article

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

2 authors.

Yifan HeSchool of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China.

Yong ZhouKey Laboratory of Advanced Theory and Application in Statistics and Data Science, Ministry of Education and Academy of Statistics and Interdisciplinary Sciences and School of Statistics, East China Normal University, Shanghai, 200062, China. yzhou@amss.ac.cn.ORCID 0000-0001-9751-3141

Funding

the State Key Program in the Major Research Plan of National Natural Science Foundation of China 91546202the State Key Program of National Natural Science Foundation of China 71931004

6 · The paper itself

Abstract

Restricted mean survival time is often of direct interest in epidemiologic studies involving censored survival time. In this article, we propose the nonparametric and semiparametric estimators of the mean restricted to the preassigned interval with censored length-biased data. Based on the peculiarity of length-biased data, the auxiliary information that truncation time and residual time have the same distribution is taken into account for improving estimation efficiency. For two-sample comparison, we construct two tests which are easy to implement. We also derive the asymptotic properties for the proposed estimators and test statistics. In simulation studies, some simulations are conducted to compare the performances of several approaches to estimate restricted mean and to assess the test statistics. In addition, our methods are applied to a real data example and some interesting results are presented.

Indexed as

Statistics, NonparametricSurvival AnalysisBiasComputer SimulationData Interpretation, StatisticalHumansSurvival RateLength-biased samplingRestricted mean survival timeRight-censored dataTreatment effectTruncated and residual time

Identifiers

PMID32300909

DOI10.1007/s10985-020-09498-x

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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.