{"doi":"10.1016/j.clon.2024.07.002","title":"Causal Inference in Oncology: Why, What, How and When","abstract":null,"journal":"Clinical Oncology","year":2025,"id":591358,"datarank":0.6943549809571457,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.2526891340821795,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.2526891340821795,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":16,"citers_with_citation_signal":8,"citers_with_endowment":8,"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":1513037,"name":"S. Elias","orcid":null,"position":1,"is_corresponding":false},{"id":1513038,"name":"R. Ranganath","orcid":null,"position":2,"is_corresponding":false},{"id":1124503,"name":"Wouter A. C. van Amsterdam","orcid":"0000-0002-3181-0810","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Causal Inference in Oncology: Why, What, How and When","abstract":"Oncologists are faced with choosing the best treatment for each patient, based on the available evidence from randomized controlled trials (RCTs) and observational studies. RCTs provide estimates of the average effects of treatments on groups of patients, but they may not apply in many real-world scenarios where for example patients have different characteristics than the RCT participants, or where different treatment variants are considered. Causal inference defines what a treatment effect is and how it may be estimated with RCTs or outside of RCTs with observational - or 'real-world' - data. In this review, we introduce the field of causal inference, explain what a treatment effect is and what important challenges are with treatment effect estimation with observational data. We then provide a framework for conducting causal inference studies and describe when in oncology causal inference from observational data may be particularly valuable. Recognizing the strengths and limitations of both RCTs and observational causal inference provides a way for more informed and individualized treatment decision-making in oncology.","is_dataset_classified":null,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39122629","pmcid":null,"openalex_id":"https://openalex.org/W4400520609","authors":[],"funders":[],"total_grants":0,"fwci":9.13,"citation_percentile":0.98538417,"influential_citations":0,"citation_trend":[{"year":2025,"count":12},{"year":2026,"count":6}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"http://www.clinicaloncologyonline.net/article/S0936655524002863/pdf","host_type":"journal"},{"url":"http://www.clinicaloncologyonline.net/article/S0936655524002863/pdf","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0936655524002863?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0936655524002863?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.clon.2024.07.002","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39122629","host_type":"repository"},{"url":"https://dspace.library.uu.nl/handle/1874/458108","host_type":"repository"}],"fields_of_study":["Advanced Causal Inference Techniques","Statistical Methods in Clinical Trials","Health Systems, Economic Evaluations, Quality of Life"],"mesh_terms":["Humans","Medical Oncology","Neoplasms","Research Design","Causality","Randomized Controlled Trials as Topic","Observational Studies as Topic"],"keywords":["Observational study","Causal inference","Randomized controlled trial","Inference","Medicine","Intensive care medicine","Medical physics","Internal medicine","Artificial intelligence","Computer science","Pathology","research methodology","Confounding","Treatment Effect Heterogeneity","Realworld Data","Individualised Cancer Care"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-25T16:00:36.332691Z","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":[]}