Evidence map›Paper›PMID 34664648›Full record

ReviewEpidemiologic reviews2022

The Measurement Error Elephant in the Room: Challenges and Solutions to Measurement Error in Epidemiology.

Gabriel K Innes, Fiona Bhondoekhan, Bryan Lau, Alden L Gross, Derek K Ng, Alison G Abraham

Abstract readReview
In one paragraph

Review in Epidemiologic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Gabriel K Innes
Fiona Bhondoekhan
Bryan Lau
Alden L Gross
Derek K Ng
Alison G Abraham

Funding

ECHODAC (Environmental Influences on Child Health Outcomes Data Analysis Center)U24OD023382 · OD · JOHNS HOPKINS UNIVERSITY · PI Diane J Catellier, LISA P JACOBSON · 2016 to 2026
$148.4M
Unsolicited R24 for the CFAR-Network of Integrated Clinical Sciences, CNICSR24AI067039 · NIAID · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Michael S. Saag · 2006 to 2026
$99.4M
Vaccine Response and Immunotherapeutics SWGP30AI094189 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Richard E. Chaisson · 2012 to 2026
$67.0M
Sex differences in the role of multi-omicsin HIV-associated carotid artery atherosclerosisU01HL146193 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Stephen J Gange, Elizabeth Topper · 2019 to 2026
$35.8M
Chronic Kidney Disease in Children Study (C-Kid)U01DK066174 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI SUSAN L. FURTH · 2003 to 2026
$25.1M
CKiD Cardiovascular Testing Travel SupplementU01DK066143 · NIDDK · CHILDREN'S MERCY HOSP (KANSAS CITY, MO) · PI Bradley Alan Warady · 2003 to 2026
$24.2M
The Kidney Disease in Children Data Management and Analysis Center (KIDMAC)U01DK066116 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI MUNOZ, ALVARO · 2003 to 2017
$17.9M
Alcohol Research Consortium in HIV: Relapse Prevention ArmP01AA029544 · NIAAA · JOHNS HOPKINS UNIVERSITY · PI CHANDER, GEETANJALI, MCCAUL, MARY E · 2021 to 2025
$7.1M
3/4 Alcohol Research Consortium in HIV - Epidemiological Research Arm (ARCH-ERA)U01AA020793 · NIAAA · UNIVERSITY OF WASHINGTON · PI CRANE, HEIDI M., LAU, BRYAN · 2011 to 2020
$5.8M
Medicaid data as a complement to cohort studies for investigating cancers among older people with HIVR01CA250851 · NCI · JOHNS HOPKINS UNIVERSITY · PI Corinne E. Joshu, Bryan Lau · 2020 to 2026
$4.8M
At the Intersection of HIV and COVID-19: Medicaid Data as a Complement to Cohort StudiesR01AI170240 · NIAID · JOHNS HOPKINS UNIVERSITY · PI JOSHU, CORINNE E., LAU, BRYAN · 2022 to 2025
$3.8M
Central Biochemistry Laboratory of the CKiD ConsortiumU01DK082194 · NIDDK · UNIVERSITY OF ROCHESTER · PI SCHWARTZ, GEORGE J · 2008 to 2017
$3.5M
NCI NIH HHS R01 CA250851NHLBI NIH HHS U01 HL146193NIAAA NIH HHS U01 AA020793NIAID NIH HHS P30 AI094189NIAID NIH HHS R01 AI170240NIAID NIH HHS R24 AI067039NIA NIH HHS R01 AG052412NIDDK NIH HHS U01 DK066116NIDDK NIH HHS U01 DK066143NIDDK NIH HHS U01 DK066174NIDDK NIH HHS U01 DK082194NIH HHS U24 OD023382
6 · The paper itself

Abstract

Measurement error, although ubiquitous, is uncommonly acknowledged and rarely assessed or corrected in epidemiologic studies. This review offers a straightforward guide to common problems caused by measurement error in research studies and a review of several accessible bias-correction methods for epidemiologists and data analysts. Although most correction methods require criterion validation including a gold standard, there are also ways to evaluate the impact of measurement error and potentially correct for it without such data. Technical difficulty ranges from simple algebra to more complex algorithms that require expertise, fine tuning, and computational power. However, at all skill levels, software packages and methods are available and can be used to understand the threat to inferences that arises from imperfect measurements.

Indexed as

BiasEpidemiologic StudiesHumansbias correctionepidemiologic methodsepidemiologic reviewmeasurement errorsensitivity analyses

Identifiers

PMID34664648
PMCPMC9005058

What OpenQuestion holds

Textmetadata
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.