ArticleGlobal epidemiology2022
Commentary: On the reliability of causal claims.
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.
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.
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.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Causal inference from observational data in neurosurgical studies: a mini-review and tutorial.Acta neurochirurgica · 2025Review
- Response: Alternative approaches for systematic review.Global epidemiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.