Evidence map›Paper›PMID 34212299›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2022

Design-Based Approaches to Causal Replication Studies.

Vivian C Wong, Kylie Anglin, Peter M Steiner

Abstract read
PubMed Publisher
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 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. 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

3 authors.

Vivian C WongUniversity of Virginia, Charlottesville, VA, USA. vcw2n@virginia.edu.ORCID 0000-0003-1037-1334
Kylie AnglinUniversity of Connecticut, Storrs, CT, USA.
Peter M SteinerUniversity of Maryland, College Park, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent interest in promoting replication efforts assumes that there is well-established methodological guidance for designing and implementing these studies. However, no such consensus exists in the methodology literature. This article addresses these challenges by describing design-based approaches for planning systematic replication studies. Our general approach is derived from the Causal Replication Framework (CRF), which formalizes the assumptions under which replication success can be expected. The assumptions may be understood broadly as replication design requirements and individual study design requirements. Replication failure occurs when one or more CRF assumptions are violated. In design-based approaches to replication, CRF assumptions are systematically tested to evaluate the replicability of effects, as well as to identify sources of effect variation when replication failure is observed. The paper describes research designs for replication and demonstrates how multiple designs may be combined in systematic replication efforts, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.

Indexed as

Research DesignCausalityHumansCausal inferenceOpen scienceReplication

Identifiers

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.