{"id":"W2133959349","doi":"10.1093/aje/kwf215","title":"Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation","year":2003,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2200,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Gee; Generalized estimating equation; Binary data; Binary number; Multivariate statistics; Simple (philosophy); Orientation (vector space); Set (abstract data type); Longitudinal data; Data set; Statistics; Estimating equations; Computer science; Mathematics; Multivariate analysis; Applied mathematics; Algorithm; Data mining; Maximum likelihood; Arithmetic","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":[0.04706145,0.001987385,0.002826239,0.005985145,0.001003242,0.004261078,0.002537044,0.002690725,0.002392418],"category_scores_gemma":[0.07611102,0.001423655,0.003368501,0.006418401,0.006851132,0.005322339,0.004675454,0.006998913,0.001206262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001898921,"about_ca_system_score_gemma":0.002706077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002860217,"about_ca_topic_score_gemma":0.001793372,"domain_scores_codex":[0.9467785,0.04296141,0.001681083,0.003254037,0.005036261,0.0002886353],"domain_scores_gemma":[0.9428446,0.04748054,0.002230061,0.004034436,0.0030868,0.0003235828],"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":[0.00004969291,0.00007550833,0.002107991,0.0008243026,0.0004528669,0.0001989416,0.0006354515,0.01078226,0.0005404725,0.8223648,0.009429604,0.1525381],"study_design_scores_gemma":[0.00007908304,0.0001335154,0.001258967,0.0004349601,0.0001508134,0.0003455681,0.0001277963,0.05844649,0.0006169514,0.8904335,0.04785554,0.0001166652],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006231639,0.003857633,0.9911665,0.00262476,0.0002995399,0.00006930155,0.0000647643,0.0001033086,0.001190993],"genre_scores_gemma":[0.02430642,0.0130722,0.9569784,0.001657422,0.001783986,0.0005880321,0.0001499998,0.0002155671,0.001247934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04706145,"threshold_uncertainty_score":0.2488878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.307497116676916,"score_gpt":0.5219258977512772,"score_spread":0.2144287810743611,"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."}}