Evidence map›Paper›PMID 29603046›Full record

ArticleLifetime data analysis2019

Bayes factors for choosing among six common survival models.

Jiajia Zhang, Timothy Hanson, Haiming Zhou

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Lifetime data analysis, 2019. 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

3 authors.

Jiajia ZhangDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, 29208, USA. jzhang@mailbox.sc.edu.
Timothy HansonMedtronic Inc., Minneapolis, MN, USA.
Haiming ZhouDivision of Statistics, Northern Illinois University, DeKalb, IL, 60115, USA.

Funding

Innovative Spatio-temporal Survival Models Allowing Crossing SurvivalR03CA176739 · NCI · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI HANSON, TIMOTHY E., ZHANG, JIAJIA · 2014 to 2015
$142k
NCI NIH HHS R03 CA176739NIH HHS 1R03CA176739-01A1
6 · The paper itself

Abstract

A super model that includes proportional hazards, proportional odds, accelerated failure time, accelerated hazards, and extended hazards models, as well as the model proposed in Diao et al. (Biometrics 69(4):840-849, 2013) accounting for crossed survival as special cases is proposed for the purpose of testing and choosing among these popular semiparametric models. Efficient methods for fitting and computing fast, approximate Bayes factors are developed using a nonparametric baseline survival function based on a transformed Bernstein polynomial. All manner of censoring is accommodated including right, left, and interval censoring, as well as data that are observed exactly and mixtures of all of these; current status data are included as a special case. The method is tested on simulated data and two real data examples. The approach is easily carried out via a new function in the spBayesSurv R package.

Indexed as

Bayes TheoremComputer SimulationSurvival AnalysisAlgorithmsBiometryData AccuracyData AnalysisData Interpretation, StatisticalHumansModels, StatisticalProportional Hazards ModelsBayes factorBernstein polynomialInterval censoringModel choice

Identifiers

PMID29603046
PMCPMC6165714

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.