{"id":"W2992636254","doi":"10.1002/sim.8414","title":"Optimizing interim analysis timing for Bayesian adaptive commensurate designs","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xenon Pharmaceuticals (Canada)","funders":"Sanofi","keywords":"Interim; Bayesian probability; Interim analysis; Computer science; Adaptive design; Econometrics; Statistics; Artificial intelligence; Mathematics; Medicine; Clinical trial; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05942666,0.001663188,0.00225733,0.001437695,0.0006296439,0.001890454,0.002467409,0.002151483,0.005359083],"category_scores_gemma":[0.143701,0.001336293,0.001582015,0.001131826,0.001748894,0.002720734,0.002575834,0.003702004,0.001158164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00155382,"about_ca_system_score_gemma":0.003654566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004971569,"about_ca_topic_score_gemma":0.0008045029,"domain_scores_codex":[0.9592894,0.03473224,0.001330396,0.001948946,0.002257947,0.0004410124],"domain_scores_gemma":[0.9175348,0.06864009,0.004986262,0.005132742,0.002702287,0.00100375],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004431246,0.0007139198,0.007162824,0.0009167575,0.0006106197,0.0002357442,0.0005571918,0.3401378,0.005588565,0.2721179,0.002920322,0.3646071],"study_design_scores_gemma":[0.001891632,0.003460356,0.002511138,0.0003747129,0.0003366344,0.0002379978,0.0000749733,0.6797807,0.006694012,0.2945146,0.009947698,0.0001754854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007120277,0.0002906604,0.9903663,0.0002585337,0.00005267375,0.0006994635,0.00006239077,0.0002235322,0.0009260948],"genre_scores_gemma":[0.1652076,0.000331224,0.8288779,0.0003797302,0.00007493908,0.003747967,0.000174724,0.0001403435,0.001065588],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9405733,"threshold_uncertainty_score":0.3142819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2877791635705335,"score_gpt":0.511439466930051,"score_spread":0.2236603033595175,"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."}}