{"id":"W2944599912","doi":"10.1186/s12874-019-0742-8","title":"The relationship between statistical power and predictor distribution in multilevel logistic regression: a simulation-based approach","year":2019,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"Learning Partnership; University of British Columbia","funders":"Lawson Foundation","keywords":"Logistic regression; Multilevel model; Statistics; Statistical power; Regression analysis; Computer science; Regression; Econometrics; Distribution (mathematics); Psychology; Mathematics","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":[0.07284988,0.0006925935,0.001352351,0.001985794,0.0007429935,0.002507523,0.002595552,0.002215934,0.004641956],"category_scores_gemma":[0.3082259,0.0007606735,0.002278549,0.001749485,0.002498228,0.002654958,0.002854037,0.002749232,0.0004561741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001844854,"about_ca_system_score_gemma":0.002410328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00239342,"about_ca_topic_score_gemma":0.001715782,"domain_scores_codex":[0.9282547,0.06467404,0.001265607,0.001901686,0.003407959,0.0004960469],"domain_scores_gemma":[0.5264463,0.4541779,0.007282035,0.007094158,0.004325812,0.0006738032],"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":[0.001095665,0.0003854256,0.07106851,0.001791243,0.002005191,0.0006424256,0.002361814,0.6457855,0.001903892,0.137047,0.003740923,0.1321725],"study_design_scores_gemma":[0.0001367695,0.0003579888,0.004653884,0.0003740259,0.0002261149,0.0002072266,0.0001623529,0.9190382,0.0009488958,0.0717715,0.002083853,0.00003907875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04951049,0.0005827714,0.9440089,0.001264451,0.00006599312,0.0005166554,0.0001216133,0.0003672025,0.003561781],"genre_scores_gemma":[0.6642774,0.000472717,0.331926,0.0003446628,0.00006445596,0.002082455,0.0001544432,0.0001802367,0.0004975418],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07284988,"threshold_uncertainty_score":0.3852716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6827948773904509,"score_gpt":0.5984411841336096,"score_spread":0.08435369325684128,"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."}}