Evidence map›Paper›PMID 37122155›Full record

ArticleStatistical methods in medical research2023

Misclassification simulation extrapolation method for a Weibull accelerated failure time model.

Varadan Sevilimedu, Lili Yu, Hani Samawi

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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.

Varadan SevilimeduDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID 0000-0002-0938-9396
Lili YuDepartment of Biostatistics, Epidemiology and Environmental Health Sciences, JPH college of Public Health, Georgia Southern University, Statesboro, GA, USA.ORCID 0000-0002-5378-3278
Hani SamawiDepartment of Biostatistics, Epidemiology and Environmental Health Sciences, JPH college of Public Health, Georgia Southern University, Statesboro, GA, USA.ORCID 0000-0003-3817-2249

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Elisa de Stanchina · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

The problem of misclassification in covariates is ubiquitous in survival data and often leads to biased estimates. The misclassification simulation extrapolation method is a popular method to correct this bias. However, its impact on Weibull accelerated failure time models has not been studied. In this paper, we study the bias caused by misclassification in one or more binary covariates in Weibull accelerated failure time models and explore the use of the misclassification simulation extrapolation in correcting for this bias, along with its asymptotic properties. Simulation studies are carried out to investigate the numerical properties of the resulting estimator for finite samples. The proposed method is then applied to colon cancer data obtained from the cancer registry at Memorial Sloan Kettering Cancer Center.

Indexed as

Proportional Hazards ModelsBiasComputer SimulationData Interpretation, StatisticalAccelerated failure time modelmeasurement errormisclassificationsurvival analysisweibull distribution

Identifiers

PMID37122155
PMCPMC10939450

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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.