Evidence map›Paper›PMID 32807591›Full record

ArticleAnnals of epidemiology2020

Reweighting to address nonparticipation and missing data bias in a longitudinal electronic health record study.

Milena A Gianfrancesco, Charles E McCulloch, Laura Trupin, Jonathan Graf, Gabriela Schmajuk, Jinoos Yazdany

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 38% of its field
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, 3 citations in OpenAlex.

  1. Article
  2. Review
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

6 authors at 2 institutions in 1 country.

Milena A GianfrancescoDivision of Rheumatology, Department of Medicine, University of California, San Francisco. Electronic address: Milena.Gianfrancesco@ucsf.edu.
Charles E McCullochDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Laura TrupinDivision of Rheumatology, Department of Medicine, University of California, San Francisco.
Jonathan GrafDivision of Rheumatology, Department of Medicine, University of California, San Francisco.
Gabriela SchmajukDivision of Rheumatology, Department of Medicine, University of California, San Francisco; Veterans Affairs Medical Center, San Francisco, CA.
Jinoos YazdanyDivision of Rheumatology, Department of Medicine, University of California, San Francisco.
University of California, San Francisco · USSan Francisco VA Medical Center · US

Funding

RESOURCE-BASED CENTER FOR THE ADVANCEMENT OF PRECISION MEDICINE IN RHEUMATOLOGYP30AR070155 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Mary C Nakamura · 2016 to 2026
$8.2M
Advancing Quality and Outcomes Measurement in RheumatologyK24AR074534 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Jinoos Yazdany · 2019 to 2026
$1.5M
Computational methods using electronic health records and registry data to detect and predict clinical outcomes in rheumatic diseaseK01AR075085 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GIANFRANCESCO, MILENA ANNE · 2019 to 2022
$458k
Examining the causal effect of sociodemographic and genetic factors on patient safety outcomes in individuals prescribed high-risk immunosuppressive medicationsF32AR070585 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GIANFRANCESCO, MILENA ANNE · 2017 to 2018
$125k
NIAMS NIH HHS F32 AR070585NIAMS NIH HHS K01 AR075085NIAMS NIH HHS K24 AR074534NIAMS NIH HHS P30 AR070155
6 · The paper itself

Abstract

purposeWe examined whether weighting techniques could account for longitudinal differences in disease activity by race/ethnicity between research participants and nonparticipants with rheumatoid arthritis (RA).

methodsWe included 377 patients with RA from a public hospital in San Francisco, CA. We estimated the probability of not enrolling in a research study by constructing weights using inverse probability weighting. Disease activity over time by race/ethnicity was analyzed across the entire patient population and among research participants only using multivariable mixed-effects models.

resultsThere were no differences in RA disease activity scores between research participants and nonparticipants at baseline; however, longitudinal differences in disease activity between research participants and nonparticipants were found by race/ethnicity. Weighting research participants in accordance with sociodemographic and clinical characteristics of the nonparticipant population did not result in any meaningful changes in disease activity by race/ethnicity over time.

conclusionsIn our study of patients with RA, inverse probability weighting using select sociodemographic and clinical variables was not sufficient to account for longitudinal disease activity differences by race/ethnicity between research participants and nonparticipants.

Indexed as

Electronic Health RecordsSelection BiasAdultAgedAged, 80 and overArthritis, RheumatoidEthnicityFemaleHospitals, PublicHumansMaleMiddle AgedProbabilitySan FranciscoElectronic health recordsGeneralizabilityInverse probability weightingSelection bias

Identifiers

PMID32807591
PMCPMC7541711
OpenAlexW3040321661

What OpenQuestion holds

Textmetadata
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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.