Evidence map›Paper›PMID 33350434›Full record

ArticleAmerican journal of epidemiology2021

A Warning About Using Predicted Values From Regression Models for Epidemiologic Inquiry.

Elizabeth L Ogburn, Kara E Rudolph, Rachel Morello-Frosch, Amber Khan, Joan A Casey

Abstract read
In one paragraph

Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Combining Observational Studies to Reduce Multiple Biases.Epidemiology (Cambridge, Mass.) · 2026
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  15. Data Integration in Causal Inference.Wiley interdisciplinary reviews. Computational statistics
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Elizabeth L Ogburn
Kara E Rudolph
Rachel Morello-Frosch
Amber Khan
Joan A Casey

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
True Metal fac coreP30ES009089 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MATTHEW S PERZANOWSKI · 1998 to 2026
$44.7M
Biostatistics, Epidemiologic & Bioinformatic Training in Environmental HealthT32ES015459 · NIEHS · UNIVERSITY OF WASHINGTON · PI June T. Spector · 2009 to 2026
$10.6M
NIEHS NIH HHS P30 ES009089NIEHS NIH HHS T32 ES015459NIH HHS U24 OD023382
6 · The paper itself

Abstract

In many settings, researchers may not have direct access to data on 1 or more variables needed for an analysis and instead may use regression-based estimates of those variables. Using such estimates in place of original data, however, introduces complications and can result in uninterpretable analyses. In simulations and observational data, we illustrate the issues that arise when an average treatment effect is estimated from data where the outcome of interest is predicted from an auxiliary model. We show that bias in any direction can result, under both the null and alternative hypotheses.

Indexed as

Data Interpretation, StatisticalEpidemiologic StudiesModels, StatisticalRegression AnalysisBiasForecastingHumansimputationmeasurement errorproxy variables

Identifiers

PMID33350434
PMCPMC8168127

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.