{"id":"W1989555992","doi":"10.1080/10543400600719251","title":"Statistical Considerations for NonInferiority/Equivalence Trials in Vaccine Development","year":2006,"lang":"en","type":"review","venue":"Journal of Biopharmaceutical Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Victoria","keywords":"Clinical trial; Equivalence (formal languages); Medicine; Sample size determination; Statistical power; Computer science; Statistics; Mathematics; Medical physics; Internal medicine","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":["metaresearch"],"category_scores_codex":[0.02894105,0.0009169264,0.01103971,0.0006225155,0.0001586685,0.0001990809,0.0005825322,0.000815313,0.0008838878],"category_scores_gemma":[0.4421055,0.0006854732,0.0009832192,0.0004517844,0.0002722773,0.00009850851,0.0001713089,0.001964766,0.00003028552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006464382,"about_ca_system_score_gemma":0.002508415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003041489,"about_ca_topic_score_gemma":0.00001134163,"domain_scores_codex":[0.9768293,0.005742534,0.01470113,0.000643389,0.001162152,0.0009215144],"domain_scores_gemma":[0.639088,0.3549614,0.004384852,0.0003221157,0.0007534434,0.0004901999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002528655,0.0008903164,0.000007673173,0.02101819,0.000650438,0.0005980468,0.00002594569,0.000001953335,0.000001943129,0.1870577,0.02755324,0.7619416],"study_design_scores_gemma":[0.002572619,0.0003056083,0.000011551,0.005210496,0.003870085,0.0001921179,0.000005200402,0.00008386434,0.00001323013,0.4778629,0.5092857,0.0005865319],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001153315,0.3693804,0.6223258,0.00007114293,0.001491255,0.001972293,0.004687844,0.00002129831,0.00004887056],"genre_scores_gemma":[0.000002257862,0.4306905,0.5682871,0.00005425487,0.000723906,0.00008987634,0.00002606726,0.00008296419,0.00004304815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7613551,"threshold_uncertainty_score":0.9999095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8715293489126777,"score_gpt":0.692162280269236,"score_spread":0.1793670686434418,"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."}}