{"id":"W3152465836","doi":"10.1002/sim.8949","title":"Optimizing subgroup selection in two‐stage adaptive enrichment and umbrella designs","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; H2020 Marie Skłodowska-Curie Actions; Innovative Medicines Initiative; Medical Research Council Canada; National Institute for Health and Care Research; Springworks Therapeutics; Medical Research Council; European Federation of Pharmaceutical Industries and Associations","keywords":"Type I and type II errors; Computer science; Selection (genetic algorithm); Word error rate; Population; Subgroup analysis; Adaptive design; Interim analysis; Bayesian probability; Multiple comparisons problem; Clinical trial; Statistics; Mathematical optimization; Medicine; Mathematics; Machine learning; Artificial intelligence; Internal medicine; Confidence interval","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004309305,0.0002248695,0.0008096652,0.000171437,0.00005620758,0.00001783217,0.0001068006,0.0001023366,0.0005477142],"category_scores_gemma":[0.0633664,0.000205806,0.00002167803,0.0005538865,0.0002625284,0.00003678091,0.0001033851,0.0005676767,0.000004650693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001935947,"about_ca_system_score_gemma":0.0001025866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001587461,"about_ca_topic_score_gemma":0.0007243395,"domain_scores_codex":[0.9963573,0.001157526,0.001105011,0.0005066892,0.0004483147,0.0004252054],"domain_scores_gemma":[0.959999,0.03929501,0.0002102237,0.0001964628,0.0001566366,0.0001427296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002426958,0.000312277,0.004044248,0.0003394568,0.00006161452,0.0008143724,0.002013305,0.0001577157,0.002209064,0.9682703,0.002007579,0.0195274],"study_design_scores_gemma":[0.004001283,0.0003681414,0.003082716,0.0005148454,0.00008531522,0.00001921058,0.001079613,0.01454592,0.0005297356,0.9753933,0.0001283403,0.0002515709],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005563106,0.0002854979,0.9909262,0.0002599701,0.0003826322,0.0004198529,0.00008895138,0.00003218092,0.002041613],"genre_scores_gemma":[0.0699938,0.000363665,0.9287739,0.0002285027,0.0001884552,0.00003676326,0.000006823702,0.0000330706,0.0003749668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0644307,"threshold_uncertainty_score":0.9445233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4502326642966361,"score_gpt":0.553600628135857,"score_spread":0.1033679638392209,"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."}}