Evidence map›Paper›PMID 35560730›Full record

ReviewResearch synthesis methods2022

Causal assessment in evidence synthesis: A methodological review of reviews.

Michal Shimonovich, Anna Pearce, Hilary Thomson, Srinivasa Vittal Katikireddi

Abstract readReview
In one paragraph

Review in Research synthesis methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Increasing the utility of epidemiologic studies as key evidence in chemical risk assessment.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
  5. 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

4 authors.

Michal ShimonovichMRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK.ORCID https://orcid.org/0000-0002-2918-0477
Anna PearceMRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Hilary ThomsonMRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Srinivasa Vittal KatikireddiMRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

Funding

Chief Scientist Office SCAF/15/02Chief Scientist Office SPHSU17Medical Research Council MC_ST_U18004Medical Research Council MC_UU_00022/2Medical Research Council MRC MC_UU_00022/2NRS Senior Clinical Fellowship SCAF/15/02Wellcome Trust 205412/Z/16/Z
6 · The paper itself

Abstract

In fields (such as population health) where randomised trials are often lacking, systematic reviews (SRs) can harness diversity in study design, settings and populations to assess the evidence for a putative causal relationship. SRs may incorporate causal assessment approaches (CAAs), sometimes called 'causal reviews', but there is currently no consensus on how these should be conducted. We conducted a methodological review of self-identifying 'causal reviews' within the field of population health to establish: (1) which CAAs are used; (2) differences in how CAAs are implemented; (3) how methods were modified to incorporate causal assessment in SRs. Three databases were searched and two independent reviewers selected reviews for inclusion. Data were extracted using a standardised form and summarised using tabulation and narratively. Fifty-three reviews incorporated CAAs: 46/53 applied Bradford Hill (BH) viewpoints/criteria, with the remainder taking alternative approaches: Medical Research Council guidance on natural experiments (2/53, 3.8%); realist reviews (2/53, 3.8%); horizontal SRs (1/53, 1.9%); 'sign test' of causal mechanisms (1/53, 1.9%); and a causal cascade model (1/53, 1.9%). Though most SRs incorporated BH, there was variation in application and transparency. There was considerable overlap across the CAAs, with a trade-off between breadth (BH viewpoints considered a greater range of causal characteristics) and depth (many alternative CAAs focused on one viewpoint). Improved transparency in the implementation of CAA in SRs in needed to ensure their validity and allow robust assessments of causality within evidence synthesis.

Indexed as

Research Designcausal assessmentcausalitypopulation healthsystematic review

Identifiers

PMID35560730
PMCPMC9543433

What OpenQuestion holds

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LicenceCC BY
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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.