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Commentary: On the reliability of causal claims
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 a...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Elsevier
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445962/ https://www.ncbi.nlm.nih.gov/pubmed/37637015 http://dx.doi.org/10.1016/j.gloepi.2022.100087 |
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author | Weed, Douglas L. |
author_facet | Weed, Douglas L. |
author_sort | Weed, Douglas L. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10445962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104459622023-08-25 Commentary: On the reliability of causal claims Weed, Douglas L. Glob Epidemiol Commentary 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. Elsevier 2022-10-17 /pmc/articles/PMC10445962/ /pubmed/37637015 http://dx.doi.org/10.1016/j.gloepi.2022.100087 Text en © 2022 The Author. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Commentary Weed, Douglas L. Commentary: On the reliability of causal claims |
title | Commentary: On the reliability of causal claims |
title_full | Commentary: On the reliability of causal claims |
title_fullStr | Commentary: On the reliability of causal claims |
title_full_unstemmed | Commentary: On the reliability of causal claims |
title_short | Commentary: On the reliability of causal claims |
title_sort | commentary: on the reliability of causal claims |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445962/ https://www.ncbi.nlm.nih.gov/pubmed/37637015 http://dx.doi.org/10.1016/j.gloepi.2022.100087 |
work_keys_str_mv | AT weeddouglasl commentaryonthereliabilityofcausalclaims |