Potential types of bias when estimating causal effects in environmental research and how to interpret them
Abstract To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Environmental Evidence |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13750-024-00324-7 |
| _version_ | 1827328642807496704 |
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| author | Ko Konno James Gibbons Ruth Lewis Andrew S Pullin |
| author_facet | Ko Konno James Gibbons Ruth Lewis Andrew S Pullin |
| author_sort | Ko Konno |
| collection | DOAJ |
| description | Abstract To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications (n = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae. |
| first_indexed | 2024-03-07T15:20:29Z |
| format | Article |
| id | doaj.art-f2cf05b8424f46b5823b45d666fd4e37 |
| institution | Directory Open Access Journal |
| issn | 2047-2382 |
| language | English |
| last_indexed | 2024-03-07T15:20:29Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Environmental Evidence |
| spelling | doaj.art-f2cf05b8424f46b5823b45d666fd4e372024-03-05T17:42:44ZengBMCEnvironmental Evidence2047-23822024-02-0113113110.1186/s13750-024-00324-7Potential types of bias when estimating causal effects in environmental research and how to interpret themKo Konno0James Gibbons1Ruth Lewis2Andrew S Pullin3School of Natural Sciences, Bangor UniversitySchool of Natural Sciences, Bangor UniversitySchool of Medical and Health Sciences, Bangor UniversitySchool of Natural Sciences, Bangor UniversityAbstract To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications (n = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae.https://doi.org/10.1186/s13750-024-00324-7Critical appraisalRisk-of-bias assessmentValidity of causal inferencesThreats to internal validity |
| spellingShingle | Ko Konno James Gibbons Ruth Lewis Andrew S Pullin Potential types of bias when estimating causal effects in environmental research and how to interpret them Environmental Evidence Critical appraisal Risk-of-bias assessment Validity of causal inferences Threats to internal validity |
| title | Potential types of bias when estimating causal effects in environmental research and how to interpret them |
| title_full | Potential types of bias when estimating causal effects in environmental research and how to interpret them |
| title_fullStr | Potential types of bias when estimating causal effects in environmental research and how to interpret them |
| title_full_unstemmed | Potential types of bias when estimating causal effects in environmental research and how to interpret them |
| title_short | Potential types of bias when estimating causal effects in environmental research and how to interpret them |
| title_sort | potential types of bias when estimating causal effects in environmental research and how to interpret them |
| topic | Critical appraisal Risk-of-bias assessment Validity of causal inferences Threats to internal validity |
| url | https://doi.org/10.1186/s13750-024-00324-7 |
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