{"id":"W1971269112","doi":"10.1081/sac-200068364","title":"Bias in Penalized Quasi-Likelihood Estimation in Random Effects Logistic Regression Models When the Random Effects Are not Normally Distributed","year":2005,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":12,"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","keywords":"Random effects model; Logistic regression; Statistics; Econometrics; Multilevel model; Estimation; Inference; Mathematics; Regression analysis; Computer science; Meta-analysis; Medicine; Artificial intelligence; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001859987,0.0002897188,0.0005904959,0.0003210946,0.0002532996,0.0001533925,0.0003787027,0.0001545312,0.000009182414],"category_scores_gemma":[0.007902221,0.0002360914,0.00004400107,0.0005252028,0.000201771,0.0003064249,0.0001711248,0.0004798337,0.000006070543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002147891,"about_ca_system_score_gemma":0.0000838945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001289265,"about_ca_topic_score_gemma":0.0008967436,"domain_scores_codex":[0.9956213,0.002239965,0.001135712,0.0003350688,0.0003599388,0.0003080319],"domain_scores_gemma":[0.965762,0.03280006,0.0005163437,0.0006230748,0.0002199044,0.00007864105],"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":[0.0007608828,0.0008799906,0.002456114,0.0004673818,0.00002620242,0.00001244964,0.003866426,0.4693929,0.00002237954,0.2496448,0.0001172665,0.2723532],"study_design_scores_gemma":[0.004713504,0.00003913246,0.01305287,0.000396062,0.00003261387,0.000001567176,0.0000666219,0.576492,0.000008445605,0.4050356,0.00001051511,0.0001511516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0147279,0.0002527737,0.9825654,0.0006587416,0.00006977473,0.001457779,0.0001035712,0.00005679239,0.0001072545],"genre_scores_gemma":[0.5909005,0.00007477577,0.4085622,0.00008629491,0.00001034632,0.0001268038,0.000218778,0.00001612204,0.000004267864],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5761726,"threshold_uncertainty_score":0.9627528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1933648142924644,"score_gpt":0.4609506508959553,"score_spread":0.2675858366034909,"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."}}