{"doi":"10.1111/cts.12764","title":"Clinical Trial Generalizability Assessment in the Big Data Era: A Review","abstract":"Clinical studies, especially randomized, controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic review to understand the practice of generalizability assessment. We identified 187 relevant articles and systematically organized these studies in a taxonomy with three dimensions: (i) data availability (i.e., before or after trial (a priori vs. a posteriori generalizability)); (ii) result outputs (i.e., score vs. nonscore); and (iii) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but < 30% of studies reported positive generalizability results. As a priori generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, < 40% of the studies in our review assessed a priori generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.","journal":"Clinical and Translational Science","year":2020,"id":52313,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":136,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9232,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":263947,"name":"Xiang D. Tang","orcid":"0000-0002-6615-3814","position":1,"is_corresponding":false},{"id":263948,"name":"Xi Yang","orcid":"0000-0003-2981-3972","position":2,"is_corresponding":false},{"id":263949,"name":"Yi Guo","orcid":"0000-0003-0587-4105","position":3,"is_corresponding":false},{"id":263950,"name":"Thomas J. George","orcid":"0000-0002-6249-9180","position":4,"is_corresponding":false},{"id":231961,"name":"Neil Charness","orcid":"0000-0002-1002-3439","position":5,"is_corresponding":false},{"id":263951,"name":"Kelsa Bartley","orcid":"0000-0002-8021-8469","position":6,"is_corresponding":false},{"id":23319,"name":"William R. Hogan","orcid":"0000-0002-9881-1017","position":7,"is_corresponding":false},{"id":23318,"name":"Jiang Bian","orcid":"0000-0002-2238-5429","position":8,"is_corresponding":false},{"id":23316,"name":"Zhe He","orcid":"0000-0003-3608-0244","position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":null,"created_at":"2026-07-18T20:42:26.843314Z","pmid":"32058639","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":[]}