Evidence map›Paper›PMID 35000097›Full record

ArticleLifetime data analysis2022

Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes.

Yanlin Tang, Xinyuan Song, Grace Yun Yi

Abstract read
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In one paragraph

Article in Lifetime data analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yanlin TangKey Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, School of Statistics, East China Normal University, Shanghai, China.ORCID 0000-0002-1950-903X
Xinyuan SongDepartment of Statistics, The Chinese University of Hong Kong, Hong Kong, People's Republic of China.
Grace Yun YiDepartment of Statistics and Actuarial Sciences, Department of Computer Science, University of Western Ontario, London, ON, Canada. gyi5@uwo.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We consider accelerated failure time models with error-prone time-to-event outcomes. The proposed models extend the conventional accelerated failure time model by allowing time-to-event responses to be subject to measurement errors. We describe two measurement error models, a logarithm transformation regression measurement error model and an additive error model with a positive increment, to delineate possible scenarios of measurement error in time-to-event outcomes. We develop Bayesian approaches to conduct statistical inference. Efficient Markov chain Monte Carlo algorithms are developed to facilitate the posterior inference. Extensive simulation studies are conducted to assess the performance of the proposed method, and an application to a study of Alzheimer's disease is presented.

Indexed as

AlgorithmsModels, StatisticalBayes TheoremComputer SimulationHumansMarkov ChainsMonte Carlo MethodAFT modelsBayesian inferenceError-prone outcomeMCMC methodsTime-to-event data

Identifiers

PMID35000097

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.