{"doi":"10.1002/pst.528","title":"Opportunities for minimization of confounding in observational research","abstract":"<jats:p>Observational epidemiological studies are increasingly used in pharmaceutical research to evaluate the safety and effectiveness of medicines. Such studies can complement findings from randomized clinical trials by involving larger and more generalizable patient populations by accruing greater durations of follow‐up and by representing what happens more typically in the clinical setting. However, the interpretation of exposure effects in observational studies is almost always complicated by non‐random exposure allocation, which can result in confounding and potentially lead to misleading conclusions. Confounding occurs when an extraneous factor, related to both the exposure and the outcome of interest, partly or entirely explains the relationship observed between the study exposure and the outcome. Although randomization can eliminate confounding by distributing all such extraneous factors equally across the levels of a given exposure, methods for dealing with confounding in observational studies include a careful choice of study design and the possible use of advanced analytical methods. The aim of this paper is to introduce some of the approaches that can be used to help minimize the impact of confounding in observational research to the reader working in the pharmaceutical industry. Copyright © 2011 John Wiley &amp; Sons, Ltd.</jats:p>","journal":"Pharmaceutical Statistics","year":2011,"id":16828,"datarank":0.7956161558349149,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.4660324692344819,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.4660324692344819,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":7,"citers_with_citation_signal":6,"citers_with_endowment":6,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":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":122686,"name":"Maurille Feudjo‐Tepie","orcid":null,"position":1,"is_corresponding":false},{"id":122687,"name":"Jixian Wang","orcid":null,"position":2,"is_corresponding":false},{"id":122688,"name":"Joseph Kim","orcid":null,"position":3,"is_corresponding":false},{"id":122685,"name":"George Quartey","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"22127842","pmcid":null,"openalex_id":"https://openalex.org/W1975046232","authors":[],"funders":[],"total_grants":0,"fwci":0.8373,"citation_percentile":0.74482249,"influential_citations":0,"citation_trend":[{"year":2013,"count":2},{"year":2016,"count":1},{"year":2017,"count":2},{"year":2022,"count":1},{"year":2026,"count":1}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fpst.528","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.528","host_type":"publisher"},{"url":"https://doi.org/10.1002/pst.528","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/22127842","host_type":"repository"},{"url":"https://researchonline.lshtm.ac.uk/id/eprint/1832181/","host_type":"repository"}],"fields_of_study":["Advanced Causal Inference Techniques","Statistical Methods in Clinical Trials","Health Systems, Economic Evaluations, Quality of Life","Medicine","Confounding Factors, Epidemiologic","Humans","Models, Statistical","Observer Variation","Research Design"],"mesh_terms":["Humans","Research Design","Models, Statistical","Observer Variation","Confounding Factors, Epidemiologic"],"keywords":["Observational study","Confounding","Medicine","Randomized controlled trial","Outcome (game theory)","Causal inference","Randomization","Research design","Intensive care medicine","Statistics","Surgery","Internal medicine","Pathology","Mathematics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-02T14:49:15.131392Z","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":[]}