{"id":"W2134800676","doi":"10.1002/pst.1721","title":"Optimal adaptive sequential designs for crossover bioequivalence studies","year":2015,"lang":"en","type":"article","venue":"Pharmaceutical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bioequivalence; Sequential analysis; Crossover; Sample size determination; Statistics; Mathematics; Crossover study; Adaptive design; Type I and type II errors; Nominal level; Computer science; Mathematical optimization; Confidence interval; Medicine; Machine learning; Clinical trial","routes":{"ca_aff":true,"ca_fund":false,"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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004662915,0.0003150142,0.0005407823,0.0001244765,0.0002348986,0.0003199013,0.0007730286,0.00009518306,0.0004835556],"category_scores_gemma":[0.01868592,0.0002466039,0.0001255622,0.0004657091,0.0009427216,0.0004023016,0.0003885938,0.0002319075,0.0004562385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002686265,"about_ca_system_score_gemma":0.0002874721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007343349,"about_ca_topic_score_gemma":0.000001395312,"domain_scores_codex":[0.9952837,0.0006494634,0.0008466383,0.0007643317,0.001755355,0.0007004713],"domain_scores_gemma":[0.9903796,0.006696524,0.0002202506,0.0003751384,0.001470114,0.0008584304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01044693,0.001078376,0.0007848894,0.00009035785,0.0007808664,0.0003267597,0.006874461,0.01382151,0.03570293,0.3807734,0.4178489,0.1314707],"study_design_scores_gemma":[0.006762294,0.002414656,0.0001385718,0.0000324527,0.0003628141,0.00006083501,0.004822372,0.4878848,0.1206402,0.2744123,0.1011669,0.001301795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003036842,0.00141792,0.9911149,0.0002582355,0.00145793,0.0006916271,0.000976675,0.00007369261,0.0009721798],"genre_scores_gemma":[0.1532418,0.00004373063,0.8444121,0.0006857688,0.0002551273,0.00009835631,0.000009271241,0.00003422445,0.001219669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4740633,"threshold_uncertainty_score":0.9999986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8262700275298006,"score_gpt":0.64127193821718,"score_spread":0.1849980893126206,"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."}}