Evidence map›Paper›PMID 37637015›Full record

ArticleGlobal epidemiology2022

Commentary: On the reliability of causal claims.

Douglas L Weed

Open access · goldAbstract read
In one paragraph

Article in Global epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 27% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
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.

Douglas L WeedDLW Consulting Services, LLC, 1302 North Oak Forest Rd., Salt Lake City, UT 84103, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Causal assessments in epidemiology are a complex process due to the many methods involved. The general scientific method lords over the process joined by study designs and statistical methods. Other methods include those that evaluate quality and bias along with the research synthesis methods such as the systematic narrative review, meta-analysis, and the criteria-based methods. When different investigators apply these methods to the same evidence and come up with different causal assessments, as described in the review by Goodman et al. in this issue, a key question becomes, how can the differences be explained? A prime candidate involves different methodologic choices. A deeper question emerges from this same situation: are the methods used for causal assessments reliable? Reliability is a hallmark of scientific practice. The methods used to make claims about causality should be reliable. Given the complexity of the causal assessment process, an objective evaluation of reliability is challenging but clearly worth the effort. Fortunately, Hill's criterion of analogy, much maligned in epidemiology, provides a clue. This commentary explores the issue of the reliability of causal claims using the Goodman et al. systematic review as its foil along with the claims by EPA, IARC, and ATSDR about the relationship between perchloroethylene and non-Hodgkin lymphoma, the claims Goodman et al. believe are wrong.

Indexed as

CausalityEpidemiologyNon-Hodgkin lymphomaPerchloroethyleneReliability

Identifiers

PMID37637015
PMCPMC10445962
OpenAlexW4306393633

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.