{"id":"W2024015933","doi":"10.1002/pst.294","title":"Sequential design approaches for bioequivalence studies with crossover designs","year":2007,"lang":"en","type":"article","venue":"Pharmaceutical Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Theratechnologies (Canada)","funders":"","keywords":"Sample size determination; Bioequivalence; Crossover; Variance (accounting); Crossover study; Statistics; Type I and type II errors; Statistical power; Clinical study design; A priori and a posteriori; Computer science; Mathematics; Econometrics; Clinical trial; Medicine; Machine learning; Placebo; Pharmacology; Pharmacokinetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007773739,0.0004404078,0.0008799425,0.00007936053,0.0002833068,0.00009160008,0.0003865692,0.0001839101,0.0001679321],"category_scores_gemma":[0.06072364,0.0003310314,0.000113928,0.0002938384,0.001238706,0.0000778929,0.0001335064,0.0004333362,0.00002683979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000168583,"about_ca_system_score_gemma":0.0001201792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001727023,"about_ca_topic_score_gemma":0.000004849627,"domain_scores_codex":[0.9957122,0.0005995682,0.00119074,0.0007026183,0.0007320321,0.001062832],"domain_scores_gemma":[0.8775157,0.1209517,0.0002961455,0.000367069,0.0004354459,0.0004339083],"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":[0.004778831,0.0006209284,0.0001382913,0.001039028,0.0007884573,0.0001395825,0.0003240432,0.00009763594,0.0006094719,0.902356,0.008539382,0.08056831],"study_design_scores_gemma":[0.003362035,0.0007173656,0.00005089609,0.0000796646,0.0009265163,0.00002295237,0.0001257416,0.008321926,0.0192227,0.9645975,0.002000994,0.0005717041],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005018375,0.0002385377,0.9954823,0.0001911178,0.0006009637,0.001803115,0.0007165315,0.0001729519,0.0002926923],"genre_scores_gemma":[0.02612049,0.00009018419,0.9723901,0.0003840389,0.0005180414,0.0001531814,0.000006064628,0.00009516204,0.0002427419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1203521,"threshold_uncertainty_score":0.9999142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9499294280480977,"score_gpt":0.6769455700599387,"score_spread":0.2729838579881589,"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."}}