{"id":"W2618160794","doi":"10.1002/cjs.11324","title":"Estimation of a generalized linear mixed model for response‐adaptive designs in multi‐centre clinical trials","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Hessian matrix; Estimator; Generalized linear mixed model; Mathematics; Generalized linear model; Generalized estimating equation; Function (biology); Statistics; Applied mathematics; Computer science; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3048924,0.003211139,0.007514762,0.004245099,0.001247469,0.004320984,0.006178008,0.006778927,0.006858842],"category_scores_gemma":[0.3687018,0.002714852,0.008220152,0.004491629,0.005003094,0.003616685,0.00396298,0.007007205,0.001294061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004719254,"about_ca_system_score_gemma":0.005908214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002465295,"about_ca_topic_score_gemma":0.001660262,"domain_scores_codex":[0.4727775,0.4998952,0.006411054,0.01121405,0.008339873,0.001362405],"domain_scores_gemma":[0.6002172,0.3542348,0.01818016,0.01820921,0.007988886,0.001169826],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008012239,0.0009966814,0.01272556,0.005734647,0.009664623,0.0007254692,0.001554773,0.4423539,0.002024949,0.2998553,0.005460781,0.2108911],"study_design_scores_gemma":[0.003348337,0.003139283,0.002840934,0.0006875925,0.001372429,0.0001472162,0.0001199532,0.8028074,0.001427795,0.1796003,0.004281324,0.0002274699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006048736,0.0006097063,0.9896064,0.0004962994,0.0001776682,0.002322351,0.0001444634,0.0002783462,0.0003158319],"genre_scores_gemma":[0.1239397,0.00052534,0.8561012,0.0006761613,0.0001541878,0.01734019,0.0003162954,0.00009110367,0.000855863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6951076,"threshold_uncertainty_score":0.8571914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7676137574481702,"score_gpt":0.6015678473685178,"score_spread":0.1660459100796524,"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."}}