Evidence map›Paper›PMID 30080695›Full record

ArticleEpidemiology (Cambridge, Mass.)2018

Analysis of Observational Self-matched Data to Examine Acute Triggers of Outcome Events with Abrupt Onset.

Elizabeth Mostofsky, Brent A Coull, Murray A Mittleman

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

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

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  5. Reply to DingAmerican journal of respiratory and critical care medicine · 2025
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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

3 authors.

Elizabeth MostofskyFrom the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Brent A CoullDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Murray A MittlemanFrom the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Marc G Weisskopf · 1985 to 2026
$44.6M
Harvard Clinical and Translational Science CenterKL2TR001100 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$8.5M
NCATS NIH HHS KL2 TR001100NIEHS NIH HHS P30 ES000002
6 · The paper itself

Abstract

Several self-matched approaches have been proposed, including case-crossover, case-time control, fixed-effects case-time control, and self-controlled case series. Rather than comparing treatment effects between different individuals, studies use these approaches to evaluate the acute effects of transient exposures, often called "triggers," by comparing outcome risk among the same individual at different times. This eliminates confounding by between-person characteristics that remain stable over time, allowing for valid analyses even in situations where information on some health behaviors is not available, such as long-term smoking history. However, to attain valid results, differences in the probability of exposure and outcome that change over time must be addressed in the design and analysis of the study. In this article, we describe the setting, assumptions and analytic options for conducting studies using self-matched data. Approaches that involve matching or a group of noncases to address time-varying confounding may have less statistical flexibility but they are powerful tools that overcome the need to assume a particular form of any time trends in potential confounders. If data are available for all of the person-time under study, there is a gain in statistical efficiency and the ability to address time-varying confounding using flexible regression models, under the strong assumption of no mis-specification of the model.

Indexed as

Case-Control StudiesConfounding Factors, EpidemiologicHumansObservational Studies as TopicProbabilityRisk Factors

Identifiers

PMID30080695
PMCPMC6167149

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.