Evidence map›Paper›PMID 33441746›Full record

ArticleScientific reports2021

Joint modelling of colorectal cancer recurrence and death after resection using multi-state model with cured fraction.

Behnaz Alafchi, Ghodratollah Roshanaei, Leili Tapak, Mohammad Abbasi, Hossein Mahjub

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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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

5 authors.

Behnaz AlafchiDepartment of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Ghodratollah RoshanaeiDepartment of Biostatistics, School of Public Health, Modeling of Noncommunicable Disease Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Leili TapakDepartment of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Mohammad AbbasiFaculty of Medicine, Shahid Beheshti Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
Hossein MahjubDepartment of Biostatistics, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran. mahjub@umsha.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Curing of colorectal cancer (CRC) occurs at the time of resection but it is not immediately observable. If the cancer is not completely eliminated, the patient will not be cured of cancer and will experience recurrence as the tumor has regrown to a detectable size. The main propose of the present study was to assess the effects of different covariates on the probability of being cured as well as the time-to-recurrence, and time-to-death in CRC patients by using multi-state cure model. The information of 283 patients with CRC, who underwent resection, from 2000 to 2015 in Imam Khomeini Hospital of Hamadan, Iran, were analyzed. The results of multi-state cure model reveal that females and who experience metastasis were more likely to be apparently cured. It has been shown that sex has a significant effect on the time-to-recurrence given patient was in the not cured group. The survival time of patients of the not cured group was affected by the stage of disease. However, the survival of the apparently cured patients were affected by age at diagnosis and metastasis status. The multi-state cure model provided a flexible framework to study the effects of prognostic factors simultaneously on the transition between different states and the probability of being apparently cured of CRC.

Indexed as

AdultAgedColorectal NeoplasmsFemaleHumansIranKaplan-Meier EstimateLogistic ModelsMaleMarkov ChainsMiddle AgedModels, StatisticalNeoplasm MetastasisNeoplasm Recurrence, LocalProbabilityRisk Factors

Identifiers

PMID33441746
PMCPMC7806811

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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.