Evidence map›Paper›PMID 25813647›Full record

ArticleBiostatistics (Oxford, England)2015

Rank-based testing of equal survivorship based on cross-sectional survival data with or without prospective follow-up.

Kwun Chuen Gary Chan, Jing Qin

Abstract read
In one paragraph

Article in Biostatistics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Kwun Chuen Gary ChanDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA kcgchan@uw.edu.
Jing QinBiostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA.

Funding

Methods of reduced-cost designs for studying disease incidence and fatality risksR01HL122212 · NHLBI · UNIVERSITY OF WASHINGTON · PI CHAN, KWUN CHUEN GARY · 2014 to 2017
$2.2M
NHLBI NIH HHS R01 HL-122212NHLBI NIH HHS R01 HL122212
6 · The paper itself

Abstract

Existing linear rank statistics cannot be applied to cross-sectional survival data without follow-up since all subjects are essentially censored. However, partial survival information are available from backward recurrence times and are frequently collected from health surveys without prospective follow-up. Under length-biased sampling, a class of linear rank statistics is proposed based only on backward recurrence times without any prospective follow-up. When follow-up data are available, the proposed rank statistic and a conventional rank statistic that utilizes follow-up information from the same sample are shown to be asymptotically independent. We discuss four ways to combine these two statistics when follow-up is present. Simulations show that all combined statistics have substantially improved power compared with conventional rank statistics, and a Mantel-Haenszel test performed the best among the proposal statistics. The method is applied to a cross-sectional health survey without follow-up and a study of Alzheimer's disease with prospective follow-up.

Indexed as

Data Interpretation, StatisticalSurvival AnalysisAlzheimer DiseaseBiomedical ResearchCross-Sectional StudiesFollow-Up StudiesHealth SurveysHumansAccelerated failure time modelBackward recurrence timeLength biased sampling

Identifiers

PMID25813647
PMCPMC4570577

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.