Evidence map›Paper›PMID 32522027›Full record

ArticleClinical trials (London, England)2020

Phenotyping issues for exploring electronic health records to design clinical trials.

Jill Schnall, LingJiao Zhang, Jinbo Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical trials (London, England), 2020. 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.

Jill SchnallDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
LingJiao ZhangDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jinbo ChenDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0003-3174-2552

Funding

Precision Assessment and Delivery of Cancer Risks in BRCA 1/2 Mutation CancersR01CA207365 · NCI · DANA-FARBER CANCER INST · PI CHEN, JINBO, REBBECK, TIMOTHY R · 2017 to 2021
$3.6M
Data and Information Integration for Risk Prediction in the Era of Big DataR01CA236468 · NCI · UNIVERSITY OF PENNSYLVANIA · PI CHEN, JINBO · 2019 to 2024
$2.2M
Statistical Methods for Analyzing Electronic Health Record DataR01HL138306 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI CHEN, JINBO · 2018 to 2020
$1.2M
NCI NIH HHS R01 CA207365NCI NIH HHS R01 CA236468NHLBI NIH HHS R01 HL138306
6 · The paper itself

Abstract

For utilizing electronic health records to help design and conduct clinical trials, an essential first step is to select eligible patients from electronic health records, that is, electronic health record phenotyping. We present two novel statistical methods that can be used in the context of electronic health record phenotyping. One mitigates the requirement for gold-standard control patients in developing phenotyping algorithms, and the other effectively corrects for bias in downstream analysis introduced by study samples contaminated by ineligible subjects.

Indexed as

Biological Variation, PopulationElectronic Health RecordsAlgorithmsClinical Trials as TopicHumansLogistic ModelsPhenotypeResearch Designanchor variablecase contaminationElectronic health recordsphenotyping

Identifiers

PMID32522027
PMCPMC7415575

What OpenQuestion holds

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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.