{"doi":"10.1016/j.jclinepi.2022.01.016","title":"Identification of threshold for large (dramatic) effects that would obviate randomized trials is not possible","abstract":"<h4>Objective</h4>To analyze distribution of \"dramatic\", large treatment effects.<h4>Study design & setting</h4>Pareto distribution modeling of previously reported cohorts of 3,486 randomized trials (RCTs) that enrolled 1,532,459 patients and 730 non-randomized studies (NRS) enrolling 1,650,658 patients.<h4>Results</h4>We calculated the Pareto α parameter, which determines the tail of the distribution for various starting points of distribution [odds ratio<sub>min</sub> (OR<sub>min</sub>)]. In default analysis using all data at OR<sub>min</sub> ≥1, Pareto distribution fit well to the treatment effects of RCTs favoring the new treatments (P = 0.21, Kolmogorov-Smirnov test) with best α = 2.32. For NRS, Pareto fit for OR<sub>min</sub> ≥2 with best α = 1.91. For RCTs, theoretical 99th percentile OR was 32.7. The actual 99th percentile OR was 25; which converted into relative risk (RR) = 7.1. The maximum observed effect size was OR = 121 (RR = 11.45). For NRS, theoretical 99th percentile was OR = 315. The actual 99th percentile OR was 294 (RR = 13). The maximum observed effect size was OR = 1473 (RR = 66).<h4>Conclusions</h4>The effects sizes observed in RCTs and NRS considerably overlap. Large effects are rare and there is no clear threshold for dramatic effects that would obviate future RCTs.","journal":"Journal of Clinical Epidemiology","year":2022,"id":6268,"datarank":0.3596842909197557,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.0,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.1443,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-05-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":18177,"name":"Benjamin Djulbegović","orcid":"0000-0003-0671-1447","position":1,"is_corresponding":false},{"id":5150,"name":"Austin J. Parish","orcid":"0000-0001-9478-1108","position":2,"is_corresponding":false},{"id":148,"name":"John P. A. Ioannidis","orcid":"0000-0003-3118-6859","position":3,"is_corresponding":false},{"id":18176,"name":"Iztok Hozo","orcid":"0000-0003-2349-5707","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","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":[]}