{"doi":"10.1136/bmj.i969","title":"Analysis of matched case-control studies","abstract":null,"journal":"BMJ","year":2016,"id":605823,"datarank":1.0030662911799773,"base_score":6.687108607866515,"endowment":6.687108607866515,"self_citation_contribution":1.0030662911799773,"citation_network_contribution":0.0,"self_endowment_contribution":1.0030662911799773,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":801,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":22723,"name":"Neil Pearce","orcid":"0000-0002-9938-7852","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Analysis of matched case-control studies","abstract":"There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a “matched analysis” is required. However, matching in a case-control study does not control for confounding by the matching factors; in fact it can introduce confounding by the matching factors even when it did not exist in the source population. Thus, a matched design may require controlling for the matching factors in the analysis. However, it is not the case that a matched design requires a matched analysis. Provided that there are no problems of sparse data, control for the matching factors can be obtained, with no loss of validity and a possible increase in precision, using a “standard” (unconditional) analysis, and a “matched” (conditional) analysis may not be required or appropriate.","is_dataset_classified":null,"base_score":6.687108607866515,"endowment":6.687108607866515,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26916049","pmcid":null,"openalex_id":"https://openalex.org/W2281397941","authors":[],"funders":[{"funder_name":"Wellcome Trust","grant_id":"097834","title":"Institutional Strategic Support Fund 2011/12."},{"funder_name":"Wellcome Trust","grant_id":"097834/Z/11/B","title":null}],"total_grants":2,"fwci":63.8681,"citation_percentile":0.999627,"influential_citations":0,"citation_trend":[{"year":2016,"count":11},{"year":2017,"count":47},{"year":2018,"count":59},{"year":2019,"count":77},{"year":2020,"count":73},{"year":2021,"count":112},{"year":2022,"count":98},{"year":2023,"count":131},{"year":2024,"count":102},{"year":2025,"count":55},{"year":2026,"count":33}],"oa_status":"hybrid","license":"cc-by-nc","oa_locations":[{"url":"https://www.bmj.com/content/bmj/352/bmj.i969.full.pdf","host_type":"journal"},{"url":"https://www.bmj.com/content/bmj/352/bmj.i969.full.pdf","host_type":"publisher"},{"url":"http://data.bmj.org/tdm/10.1136/bmj.i969","host_type":"publisher"},{"url":"https://syndication.highwire.org/content/doi/10.1136/bmj.i969","host_type":"publisher"},{"url":"https://doi.org/10.1136/bmj.i969","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/26916049","host_type":"repository"},{"url":"https://researchonline.lshtm.ac.uk/view/creators/emsunpea.html>;","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4770817","host_type":"repository"},{"url":"http://www.bmj.com/cgi/content/short/352/feb25_6/i969","host_type":"repository"},{"url":"http://dx.doi.org/10.1136/bmj.i969","host_type":""},{"url":"https://researchonline.lshtm.ac.uk/id/eprint/2534120/1/bmj.i969.full.pdf","host_type":""},{"url":"https://dx.doi.org/10.1136/bmj.i969","host_type":""}],"fields_of_study":["Advanced Causal Inference Techniques","Statistical Methods and Inference","Statistical Methods and Bayesian Inference","03 medical and health sciences","0302 clinical medicine","Case-Control Studies","Confounding Factors, Epidemiologic","Control Groups","Data Interpretation, Statistical"],"mesh_terms":["Data Interpretation, Statistical","Confounding Factors, Epidemiologic","Case-Control Studies","Control Groups"],"keywords":["Matching (statistics)","Confounding","Computer science","Control (management)","Population","Statistics","Econometrics","Mathematics","Artificial intelligence","Medicine","Environmental health","Case-Control Studies","Data Interpretation, Statistical","Research Methods & Reporting","Confounding Factors, Epidemiologic","Control Groups"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T03:20:22.804220Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}