{"doi":"10.1016/j.jclinepi.2018.11.001","title":"Marginal structural models and other analyses allow multiple estimates of treatment effects in randomized clinical trials: Meta-epidemiological analysis","abstract":"<h4>Objectives</h4>To determine how marginal structural models (MSMs), which are increasingly used to estimate causal effects, are used in randomized clinical trials (RCTs) and compare their results with those from intention-to-treat (ITT) or other analyses.<h4>Study design and setting</h4>We searched PubMed, Scopus, citations of key references, and Clinicaltrials.gov. Eligible RCTs reported clinical effects based on MSMs and at least one other analysis.<h4>Results</h4>We included 12 RCTs reporting 138 analyses for 24 clinical questions. In 19/24 (79%), MSM-based and other effect estimates were all in the same direction, 22/22 had overlapping 95% confidence intervals (CIs), and in 19/22 (86%), the MSM effect estimate lay within all 95% CIs of all other effects (in two cases no CIs were reported). For the same clinical question, the largest effect estimate from any analysis was 1.19-fold (median; interquartile range 1.13-1.34) larger than the smallest. All MSM and ITT effect estimates were in the same direction and had overlapping 95% CIs. In 71% (12/17), they also agreed on the presence of statistical significance. MSM-based effect estimates deviated more from the null than those based on ITT (P = 0.18). The effect estimates of both approaches differed 1.12-fold (median; interquartile range 1.02-1.22).<h4>Conclusions</h4>MSMs provided largely similar effect estimates as other available analyses. Nevertheless, some of the differences in effect estimates or statistical significance may become important in clinical decision-making, and the multiple estimates require utmost attention of possible selective reporting bias.","journal":"Journal of Clinical Epidemiology","year":2019,"id":10512,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0395,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2019-03-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":31041,"name":"Benjamin Speich","orcid":"0000-0002-3301-8085","position":1,"is_corresponding":false},{"id":35798,"name":"Aviv Ladanie","orcid":"0000-0003-1050-3536","position":2,"is_corresponding":false},{"id":35803,"name":"Heiner C. Bucher","orcid":"0000-0002-0131-7873","position":3,"is_corresponding":false},{"id":3920,"name":"Lars G. Hemkens","orcid":"0000-0002-3444-1432","position":5,"is_corresponding":false},{"id":148,"name":"John P. A. Ioannidis","orcid":"0000-0003-3118-6859","position":6,"is_corresponding":false},{"id":35802,"name":"Hannah Ewald","orcid":"0000-0002-5081-1093","position":0,"is_corresponding":true}],"reference_count":69,"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":[]}