Evidence map›Paper›PMID 31128619›Full record

ArticleSpatial and spatio-temporal epidemiology2019

A data-driven approach for estimating the change-points and impact of major events on disease risk.

R Carroll, A B Lawson, S Zhao

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Spatial and spatio-temporal epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

3 authors.

R CarrollBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr., Research Triangle Park, NC, USA. Electronic address: rachel.carroll@nih.gov.
A B LawsonDepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
S ZhaoBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr., Research Triangle Park, NC, USA.

Funding

Application of Statistical Methods in Epidemiology StudiesZIAES103308 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI ZHAO, SHANSHAN · 2015 to 2025
$2.2M
Intramural NIH HHS Z99 ES999999Intramural NIH HHS ZIA ES103308
6 · The paper itself

Abstract

Considering the impact of events on disease risk is important. Here, a Bayesian spatio-temporal accelerated failure time model furnished an ideal situation for modeling events that could impact survival experience via spatial and temporal frailty estimates. Through a hierarchical structure, this model allowed the data to detect the change-point(s) in addition to generating the event-related estimates. Both a real data case study and a simulation study were employed for testing these methods. The results suggested that meaningful and accurate change-points could be detected. Further, accurate event-related estimates for individuals in relation to those change-points could be obtained. By allowing the data to drive the change-point choices, the models were better fitting and the inference was more accurate.

Indexed as

Cyclonic StormsBreast NeoplasmsFemaleGenes, BRCA1HumansLouisianaModels, StatisticalRegistriesSEER ProgramSurvival AnalysisUnited StatesWomen's HealthAccelerated failure timeBreast cancerChange-point estimationEvent impactSurvival

Identifiers

PMID31128619
PMCPMC7971716

What OpenQuestion holds

Textmetadata
LicenceTDM
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.