Identification of causal effects in case-control studies
Abstract Background Case-control designs are an important yet commonly misunderstood tool in the epidemiologist’s arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Re...
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| Format: | Article |
| Language: | English |
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BMC
2022-01-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-021-01484-7 |
| _version_ | 1818750300743270400 |
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| author | Bas B. L. Penning de Vries Rolf H. H. Groenwold |
| author_facet | Bas B. L. Penning de Vries Rolf H. H. Groenwold |
| author_sort | Bas B. L. Penning de Vries |
| collection | DOAJ |
| description | Abstract Background Case-control designs are an important yet commonly misunderstood tool in the epidemiologist’s arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Results We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Conclusion The modern epidemiologist’s arsenal for causal inference is well-suited to make transparent for case-control designs what assumptions are necessary or sufficient to endow the respective study results with a causal interpretation and, in turn, help resolve or prevent misunderstanding. Our approach may inform future research on different estimands, other variations of the case-control design or settings with additional complexities. |
| first_indexed | 2024-12-18T04:17:29Z |
| format | Article |
| id | doaj.art-fe1c96a21d3a438aa11734b0c2da3a7f |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-18T04:17:29Z |
| publishDate | 2022-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-fe1c96a21d3a438aa11734b0c2da3a7f2022-12-21T21:21:19ZengBMCBMC Medical Research Methodology1471-22882022-01-012211810.1186/s12874-021-01484-7Identification of causal effects in case-control studiesBas B. L. Penning de Vries0Rolf H. H. Groenwold1Department of Clinical Epidemiology, Leiden University Medical CenterDepartment of Clinical Epidemiology, Leiden University Medical CenterAbstract Background Case-control designs are an important yet commonly misunderstood tool in the epidemiologist’s arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Results We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Conclusion The modern epidemiologist’s arsenal for causal inference is well-suited to make transparent for case-control designs what assumptions are necessary or sufficient to endow the respective study results with a causal interpretation and, in turn, help resolve or prevent misunderstanding. Our approach may inform future research on different estimands, other variations of the case-control design or settings with additional complexities.https://doi.org/10.1186/s12874-021-01484-7Causal inferenceCase-control designsIdentifiability |
| spellingShingle | Bas B. L. Penning de Vries Rolf H. H. Groenwold Identification of causal effects in case-control studies BMC Medical Research Methodology Causal inference Case-control designs Identifiability |
| title | Identification of causal effects in case-control studies |
| title_full | Identification of causal effects in case-control studies |
| title_fullStr | Identification of causal effects in case-control studies |
| title_full_unstemmed | Identification of causal effects in case-control studies |
| title_short | Identification of causal effects in case-control studies |
| title_sort | identification of causal effects in case control studies |
| topic | Causal inference Case-control designs Identifiability |
| url | https://doi.org/10.1186/s12874-021-01484-7 |
| work_keys_str_mv | AT basblpenningdevries identificationofcausaleffectsincasecontrolstudies AT rolfhhgroenwold identificationofcausaleffectsincasecontrolstudies |