{"id":"W2006031679","doi":"10.1186/1471-2288-7-34","title":"A simulation study of sample size for multilevel logistic regression models","year":2007,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":400,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; St. Michael's Hospital; University of Toronto","funders":"Ontario Ministry of Health and Long-Term Care","keywords":"Statistics; Sample size determination; Multilevel model; Logistic regression; Random effects model; Variance (accounting); Covariance; Mathematics; Hierarchical database model; Sample (material); Econometrics; Regression; Medicine; Computer science; Meta-analysis; Data mining","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":[0.05461846,0.000915547,0.001807696,0.001683592,0.001123859,0.001774447,0.002634742,0.002461245,0.003390787],"category_scores_gemma":[0.2355839,0.0007196746,0.002125658,0.002125953,0.001642594,0.002458331,0.002056265,0.003197763,0.0002298751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002700877,"about_ca_system_score_gemma":0.002232731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0130631,"about_ca_topic_score_gemma":0.006774671,"domain_scores_codex":[0.9714386,0.02530128,0.0005071524,0.001106211,0.001083866,0.0005629908],"domain_scores_gemma":[0.4851392,0.4912776,0.00743014,0.00661793,0.008253897,0.001281176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009392232,0.0003417666,0.03924755,0.0003933221,0.0004723324,0.0005544825,0.0007802476,0.9027356,0.0003450244,0.03671673,0.00199189,0.01548188],"study_design_scores_gemma":[0.0001115586,0.0001666572,0.001670712,0.00006925201,0.00007640014,0.0000813584,0.0001384847,0.9893507,0.0001444935,0.007817791,0.0003536364,0.00001901196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5004105,0.001349083,0.4851601,0.002603449,0.000187235,0.001368552,0.001015506,0.0004592039,0.007446415],"genre_scores_gemma":[0.8857378,0.0003355983,0.1106632,0.0002445157,0.00004077831,0.001374129,0.0006182811,0.00007007657,0.0009157039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05461846,"threshold_uncertainty_score":0.2888535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8745461031859402,"score_gpt":0.6879301196957895,"score_spread":0.1866159834901506,"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."}}