Evidence map›Paper›PMID 29772007›Full record

Observational studyPloS one2018

Nonparametric estimation of median survival times with applications to multi-site or multi-center studies.

Mohammad H Rahbar, Sangbum Choi, Chuan Hong, Liang Zhu, Sangchoon Jeon, Joseph C Gardiner

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in PloS one, 2018. 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

6 authors.

Mohammad H RahbarDepartment of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas School of Public Health at Houston, Houston, TX 77030, United States of America.ORCID 0000-0003-4608-9445
Sangbum ChoiDepartment of Statistics, Korea University, Seoul, South Korea.
Chuan HongDepartment of Biostatistics, Harvard University, Boston, MA 02115, United States of America.
Liang ZhuDivision of Clinical and Translational Sciences, Department of Internal Medicine, McGovern Medical School; University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America.
Sangchoon JeonDivision of Acute Care/Health Systems, School of Nursing, Yale University, West Haven, CT 27399, United States of America.
Joseph C GardinerDepartment of Epidemiology and Biostatistics, Michigan State University, Lansing, MI 48824, United States of America.

Funding

U OF TEXAS HLTH SCI CTR HOUSTON CENTER FOR CLINICAL AND TRANSLATIONAL SCIENCESUL1RR024148 · NCRR · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MCPHERSON, DAVID D · 2006 to 2010
$27.9M
Center for Clinical and Translational Sciences (CCTS)UL1TR000371 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MCPHERSON, DAVID D · 2012 to 2016
$13.4M
Statistical Methodology Development in Blood Transfusion Protocol ResearchR21HL109479 · NHLBI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI NING, JING, RAHBAR, MOHAMMAD HOSSEIN · 2013 to 2014
$418k
NCATS NIH HHS UL1 TR000371NCRR NIH HHS UL1 RR024148NHLBI NIH HHS R21 HL109479
6 · The paper itself

Abstract

We propose a nonparametric shrinkage estimator for the median survival times from several independent samples of right-censored data, which combines the samples and hypothesis information to improve the efficiency. We compare efficiency of the proposed shrinkage estimation procedure to unrestricted estimator and combined estimator through extensive simulation studies. Our results indicate that performance of these estimators depends on the strength of homogeneity of the medians. When homogeneity holds, the combined estimator is the most efficient estimator. However, it becomes inconsistent when homogeneity fails. On the other hand, the proposed shrinkage estimator remains efficient. Its efficiency decreases as the equality of the survival medians is deviated, but is expected to be as good as or equal to the unrestricted estimator. Our simulation studies also indicate that the proposed shrinkage estimator is robust to moderate levels of censoring. We demonstrate application of these methods to estimating median time for trauma patients to receive red blood cells in the Prospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study.

Indexed as

Multicenter Studies as TopicStatistics, NonparametricSurvival AnalysisBlood TransfusionComputer SimulationData Interpretation, StatisticalHumansProspective StudiesTime FactorsTime-to-TreatmentWounds and Injuries

Identifiers

PMID29772007
PMCPMC5957417

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.