{"id":"W4403497067","doi":"10.1002/sim.10247","title":"A Nonparametric Global Win Probability Approach to the Analysis and Sizing of Randomized Controlled Trials With Multiple Endpoints of Different Scales and Missing Data: Beyond O'Brien–Wei–Lachin","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Statistics; Confidence interval; Nonparametric statistics; Coverage probability; Point estimation; Mathematics; Type I and type II errors; Clinical endpoint; Sample size determination; Computer science; Randomized controlled trial; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09065206,0.001936667,0.004863268,0.005475249,0.0008954757,0.004932262,0.003360642,0.002405334,0.01004393],"category_scores_gemma":[0.2806495,0.001450437,0.004076062,0.004301346,0.004076152,0.005843852,0.004053096,0.005773348,0.001342866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002601168,"about_ca_system_score_gemma":0.006450843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878773,"about_ca_topic_score_gemma":0.001610482,"domain_scores_codex":[0.9031178,0.07620274,0.00496022,0.005449316,0.009592624,0.0006773072],"domain_scores_gemma":[0.7248388,0.2430974,0.008775979,0.01440327,0.007697619,0.001186911],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00166822,0.000160848,0.004278233,0.002308825,0.001388115,0.0004292262,0.000703753,0.142773,0.001554583,0.3881121,0.0109543,0.4456688],"study_design_scores_gemma":[0.0003973123,0.0005103294,0.001060518,0.0005777156,0.0002778378,0.0002295207,0.00008298561,0.4501718,0.001446477,0.5308893,0.01425414,0.0001020676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001083344,0.0004171383,0.9963437,0.0003428112,0.00007658167,0.0006010434,0.000186398,0.0002687276,0.000680367],"genre_scores_gemma":[0.03789369,0.0005951247,0.9564937,0.0005391389,0.0001617855,0.002896626,0.0003522511,0.0002218821,0.000845748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.909348,"threshold_uncertainty_score":0.4794196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.303822623694461,"score_gpt":0.5183622358018237,"score_spread":0.2145396121073627,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}