{"id":"W4361269722","doi":"10.1080/19466315.2023.2197402","title":"Application of Group Sequential Methods to the 2-in-1 Design and Its Extensions for Interim Monitoring","year":2023,"lang":"en","type":"article","venue":"Statistics in Biopharmaceutical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Michael Smith Health Research BC","keywords":"Interim; Interim analysis; Group (periodic table); Adaptive design; Research design; Computer science; Reliability engineering; Medicine; Statistics; Risk analysis (engineering); Medical physics; Mathematics; Randomized controlled trial; Clinical trial; Engineering; Internal medicine; Political science; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03268167,0.0001510473,0.0004607827,0.0004129857,0.0001197769,0.00003989044,0.0004352177,0.0001447976,0.00002455295],"category_scores_gemma":[0.182667,0.0001179226,0.00004157302,0.001429645,0.0003058817,0.00003101149,0.0004840435,0.0007097338,0.00002371002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001067811,"about_ca_system_score_gemma":0.00007079803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001786862,"about_ca_topic_score_gemma":0.000005938592,"domain_scores_codex":[0.9933337,0.003794703,0.0009987764,0.0005119043,0.0006583885,0.0007025245],"domain_scores_gemma":[0.8217584,0.1772919,0.00007193394,0.0003100073,0.0003607041,0.0002069862],"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":[0.0009251073,0.0002213499,0.0002152903,0.0005698855,0.0000347141,0.00002340523,0.0003993096,0.00009214605,0.08954758,0.7097322,0.0009638307,0.1972752],"study_design_scores_gemma":[0.0007874584,0.0002345849,0.001231418,0.0001147602,0.00002393454,0.000001301993,0.0001528189,0.1429099,0.02370946,0.8301342,0.0005752034,0.0001250581],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004427308,0.0000781246,0.9912073,0.0008124437,0.0003863331,0.002751359,0.0002736002,0.00003129875,0.00003221376],"genre_scores_gemma":[0.1012912,0.0002203465,0.8973579,0.00002761149,0.0001523474,0.0008679338,0.000002804078,0.00003692181,0.00004290905],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1971501,"threshold_uncertainty_score":0.9960577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9038046754759503,"score_gpt":0.7565153356159264,"score_spread":0.1472893398600239,"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."}}