Evidence map›Paper›PMID 38505284›Full record

ArticleAnnals of clinical epidemiology2022

Introduction to Regression Discontinuity Design.

Yusuke Sasabuchi

Abstract read
In one paragraph

Article in Annals of clinical epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Causal Inference in Clinical Research: A Primer.Journal of periodontal research · 2026
    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

1 author.

Yusuke SasabuchiData Science Center, Jichi Medical University.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is common clinical practice for physicians to refer to specific diagnostic criteria for day-to-day decision-making. In particular, whether or not to provide a particular treatment is often determined by the cutoff value of a relevant diagnostic marker. Regression discontinuity design (RDD) is a method for evaluating scenarios where intervention is determined by the certain cutoff value (e.g., threshold) of a continuous variable. RDD represents a powerful method for assessing intervention effects and outcomes. RDD is underutilized in clinical research and there are many opportunities to apply RDD in this setting. This article introduces the principles of RDD and provides examples of clinical studies that have used this design.

Indexed as

fuzzy regression discontinuityinstrumental variableregression discontinuity designsharp regression discontinuity

Identifiers

PMID38505284
PMCPMC10760478

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.