{"id":"W2093789600","doi":"10.2202/1557-4679.1195","title":"Estimating Multilevel Logistic Regression Models When the Number of Clusters is Low: A Comparison of Different Statistical Software Procedures","year":2010,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":192,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Statistics; Multilevel model; Logistic regression; Computer science; Variance (accounting); Statistical model; Hierarchical database model; Software; Bayesian probability; Mathematics; Data mining; Econometrics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.1030645,0.00101342,0.002631733,0.003771267,0.00115267,0.002634489,0.003874955,0.001745467,0.003987554],"category_scores_gemma":[0.4144878,0.001079323,0.003691606,0.005520726,0.001786607,0.004102206,0.005071732,0.00326789,0.0007135675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001933232,"about_ca_system_score_gemma":0.003858201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005369713,"about_ca_topic_score_gemma":0.009124969,"domain_scores_codex":[0.8635456,0.1191008,0.004669376,0.003960647,0.007822404,0.0009011734],"domain_scores_gemma":[0.5658888,0.3932725,0.01020294,0.01912528,0.01048255,0.001027937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004697191,0.0008211776,0.08773332,0.005103732,0.007028612,0.0007100552,0.0107267,0.09211794,0.004831906,0.09898749,0.0118589,0.6753829],"study_design_scores_gemma":[0.001609992,0.001955288,0.06676786,0.001959564,0.002971149,0.0008129569,0.00280271,0.6859221,0.008030606,0.2077345,0.01879312,0.0006402346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06604794,0.0007301414,0.9288916,0.0007465938,0.00007119952,0.0007886959,0.0004344067,0.0009717831,0.001317644],"genre_scores_gemma":[0.1404551,0.000476541,0.8554844,0.0001903137,0.00003247275,0.002012886,0.0004875823,0.000534041,0.0003267649],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1030645,"threshold_uncertainty_score":0.5450636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08904626714055525,"score_gpt":0.4328148997691603,"score_spread":0.343768632628605,"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."}}