{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.127512,0.003069204,0.003774009,0.00357178,0.001131856,0.00212869,0.003797135,0.003276133,0.01313451],"category_scores_gemma":[0.180698,0.00156412,0.003950695,0.004106804,0.003789415,0.002947705,0.002761969,0.00622269,0.001675003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002559015,"about_ca_system_score_gemma":0.005419191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001132763,"about_ca_topic_score_gemma":0.001122242,"domain_scores_codex":[0.8137314,0.1629213,0.004767352,0.005721753,0.01206808,0.0007899709],"domain_scores_gemma":[0.8719517,0.1096344,0.005076703,0.007642461,0.005108746,0.000585979],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002816554,0.0006165943,0.001215793,0.003212715,0.00146169,0.0002165651,0.001695381,0.05478215,0.002104336,0.6412054,0.004730184,0.2859427],"study_design_scores_gemma":[0.006112018,0.007127528,0.001211644,0.0007302428,0.0007821782,0.0002596382,0.0001688922,0.257008,0.00334117,0.6835752,0.03946261,0.00022098],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008692722,0.0002738079,0.9956524,0.0001025662,0.0001718563,0.002139414,0.00005467224,0.000117106,0.0006188018],"genre_scores_gemma":[0.01828115,0.0004463896,0.9669777,0.0002045222,0.0001643843,0.01315973,0.0000736445,0.00006171911,0.0006306703],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.872488,"threshold_uncertainty_score":0.6743559,"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."}}