{"id":"W2911938742","doi":"10.1002/cjs.11482","title":"A consistent estimator for logistic mixed effect models","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; Huntington Society of Canada; Hewlett-Packard Development Company; National Science Foundation","keywords":"Estimator; Covariate; Random effects model; Econometrics; Normality; Logistic regression; Statistics; Mixed model; Independence (probability theory); Mathematics; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02871483,0.00137725,0.003108339,0.002982277,0.0006929365,0.002760659,0.004482585,0.002944499,0.004252442],"category_scores_gemma":[0.1263027,0.001419088,0.00286367,0.002521845,0.002156571,0.003455244,0.003347452,0.004509827,0.002106171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009670873,"about_ca_system_score_gemma":0.003096622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002088657,"about_ca_topic_score_gemma":0.002160077,"domain_scores_codex":[0.9805634,0.01586243,0.0006155843,0.001358012,0.001302085,0.0002985911],"domain_scores_gemma":[0.9313027,0.05715503,0.003056929,0.00423687,0.003761663,0.0004868416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003949346,0.000236112,0.01702682,0.001526295,0.001533591,0.0006537426,0.0008277411,0.1963627,0.003349396,0.3822144,0.01386491,0.3820093],"study_design_scores_gemma":[0.0002337384,0.0003116317,0.001947374,0.0004975839,0.0003834876,0.0005505449,0.0001974821,0.6896405,0.001695597,0.2879703,0.01642041,0.000151298],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000727089,0.0001652813,0.9986156,0.0001244702,0.00002190815,0.00004226041,0.0000587189,0.0001187579,0.0001259045],"genre_scores_gemma":[0.05096396,0.000615534,0.9449151,0.0004209833,0.0001966183,0.0009414037,0.0006615731,0.0002517911,0.001033055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02871483,"threshold_uncertainty_score":0.1518604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5403284648349026,"score_gpt":0.5035855842461586,"score_spread":0.03674288058874398,"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."}}