Evidence map›Paper›PMID 33133648›Full record

ArticleThe R journal2020

CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model.

Chi Hyun Lee, Heng Zhou, Jing Ning, Diane D Liu, Yu Shen

Abstract read
In one paragraph

Article in The R journal, 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.

  1. 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

5 authors.

Chi Hyun LeeDepartment of Biostatistics and Epidemiology, University of Massachusetts Amherst.
Heng ZhouBiostatistics and Research Decision Sciences, Merck & Co., Inc.
Jing NingDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center.
Diane D LiuDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center.
Yu ShenDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Comparative Effectiveness of Cancer Research: Use Data from Multiple SourcesR01CA193878 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI NING, JING · 2016 to 2019
$1.2M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA193878
6 · The paper itself

Abstract

Data subject to length-biased sampling are frequently encountered in various applications including prevalent cohort studies and are considered as a special case of left-truncated data under the stationarity assumption. Many semiparametric regression methods have been proposed for length-biased data to model the association between covariates and the survival outcome of interest. In this paper, we present a brief review of the statistical methodologies established for the analysis of length-biased data under the Cox model, which is the most commonly adopted semiparametric model, and introduce an R package

Identifiers

PMID33133648
PMCPMC7595345

What OpenQuestion holds

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Registered trials

None linked

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.