Evidence map›Paper›PMID 34343240›Full record

ArticleAmerican journal of epidemiology2021

Synthetic Control Methods for the Evaluation of Single-Unit Interventions in Epidemiology: A Tutorial.

Carl Bonander, David Humphreys, Michelle Degli Esposti

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 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
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  8. Review
  9. Children, vaccines, and financial incentives.International journal of health economics and management · 2023
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  12. Article
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  16. Review
  17. Article
  18. Changes to ACA Individual Insurance Markets After States Leave Healthcare.gov 2016-2023.Inquiry : a journal of medical care organization, provision and financing
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Carl Bonander
David Humphreys
Michelle Degli Esposti

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating the impacts of population-level interventions (e.g., changes to state legislation) can be challenging as conducting randomized experiments is often impractical and inappropriate, especially in settings where the intervention is implemented in a single, aggregate unit (e.g., a country or state). A common nonrandomized alternative is to compare outcomes in the treated unit(s) with unexposed controls both before and after the intervention. However, the validity of these designs depends on the use of controls that closely resemble the treated unit on before-intervention characteristics and trends on the outcome, and suitable controls may be difficult to find because the number of potential control regions is typically limited. The synthetic control method provides a potential solution to these problems by using a data-driven algorithm to identify an optimal weighted control unit-a "synthetic control"-based on data from before the intervention from available control units. While popular in the social sciences, the method has not garnered as much attention in health research, perhaps due to a lack of accessible texts aimed at health researchers. We address this gap by providing a comprehensive, nontechnical tutorial on the synthetic control method, using a worked example evaluating Florida's "stand your ground" law to illustrate methodological and practical considerations.

Indexed as

Data Interpretation, StatisticalEpidemiologic MethodsHumansReproducibility of Resultscausal inferenceinternal validitypanel dataprogram evaluationquasi-experiments

Identifiers

PMID34343240
PMCPMC8634614

What OpenQuestion holds

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LicenceCC BY
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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.