{"id":"W2016120509","doi":"10.2307/3315998","title":"Analyzing multivariate longitudinal binary data: A generalized estimating equations approach","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Generalized estimating equation; Multivariate statistics; Random effects model; Binary data; Binary number; Statistics; Variance (accounting); Mathematics; Estimating equations; Longitudinal data; Applied mathematics; Mixed model; Maximum likelihood; Computer science; Econometrics; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0217412,0.001528628,0.002697116,0.003512506,0.0008094331,0.00185195,0.003874986,0.001875313,0.002715582],"category_scores_gemma":[0.06810173,0.001197165,0.002763715,0.004903872,0.001247662,0.001770601,0.002629553,0.003161603,0.0006113633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001404664,"about_ca_system_score_gemma":0.002587021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01334258,"about_ca_topic_score_gemma":0.01028377,"domain_scores_codex":[0.9693731,0.02742039,0.0005750132,0.001134246,0.001164949,0.0003322456],"domain_scores_gemma":[0.9528372,0.04099743,0.002203214,0.002396194,0.001328158,0.0002377498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002057039,0.0003539931,0.02093668,0.0007090346,0.004105106,0.0005536468,0.0006585232,0.3741947,0.00102972,0.3450947,0.007873742,0.2442846],"study_design_scores_gemma":[0.0001182681,0.0001255487,0.0027335,0.0001088648,0.0003260113,0.0000872982,0.00009273289,0.7611259,0.0003007283,0.2308468,0.004072571,0.0000617203],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005605966,0.0005282313,0.9925884,0.000510252,0.00004470849,0.00008283527,0.0002110729,0.0001532307,0.0002752358],"genre_scores_gemma":[0.1222363,0.001271205,0.8734454,0.0002496883,0.0001794683,0.0008213534,0.0008093957,0.00009143716,0.0008957745],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0217412,"threshold_uncertainty_score":0.1149798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1933279083612514,"score_gpt":0.3871777933494236,"score_spread":0.1938498849881722,"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."}}