{"id":"W7014010608","doi":"","title":"OPTIMIZED ADAPTIVE ENRICHMENT DESIGNS FOR MULTI-ARM TRIALS: LEARNING WHICH SUBPOPULATIONS BENEFIT FROM DIFFERENT TREATMENTS","year":2018,"lang":"en","type":"article","venue":"Collection of Biostatistics Research Archive","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Food and Drug Administration; Hamilton Health Sciences Foundation","keywords":"Sample size determination; Adaptive design; Type I and type II errors; Sample (material); Disjoint sets; Adaptive control; Computerized adaptive testing; Interim analysis","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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006932391,0.0003332663,0.001363343,0.0005641145,0.0009023541,0.00008553618,0.0003419827,0.0001948274,0.0005891737],"category_scores_gemma":[0.2661201,0.0002786685,0.0002620748,0.0007866484,0.00060825,0.0000528723,0.0002096989,0.0005657766,0.00002217588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003754181,"about_ca_system_score_gemma":0.0002700905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004266587,"about_ca_topic_score_gemma":0.0004291711,"domain_scores_codex":[0.990072,0.005699434,0.001807828,0.0007467337,0.0009939797,0.0006800272],"domain_scores_gemma":[0.8071021,0.1896021,0.0006974742,0.0004318925,0.00185135,0.0003150165],"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":[0.06105485,0.01690121,0.004968987,0.0007195045,0.009847881,0.00003014452,0.0139585,0.00102008,0.0313544,0.6592631,0.04468342,0.156198],"study_design_scores_gemma":[0.006831061,0.004977264,0.003230431,0.0001624599,0.0003428648,7.232444e-7,0.000418079,0.1397109,0.008136091,0.8358158,0.00012003,0.0002543092],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005644624,0.00002732761,0.9850083,0.00009254707,0.0005244254,0.003714254,0.004371895,0.000070139,0.0005465119],"genre_scores_gemma":[0.06781724,0.0001407403,0.9292521,0.000008031409,0.0003637244,0.0008051204,0.0001521513,0.00006658682,0.001394232],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2591877,"threshold_uncertainty_score":0.9999666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8219072492581264,"score_gpt":0.6271974068010037,"score_spread":0.1947098424571228,"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."}}