{"id":"W2061663271","doi":"10.1016/j.jspi.2012.04.006","title":"Marginal methods for clustered longitudinal binary data with incomplete covariates","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Mathematics; Estimator; Statistics; Missing data; Marginal model; Estimating equations; Longitudinal data; Econometrics; Binary data; Random effects model; Generalized estimating equation; Data set; Binary number; Regression analysis; Data mining; Medicine; Computer science; Meta-analysis","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":[0.002494901,0.0002083208,0.0005837108,0.0001008019,0.0001464246,0.00009905485,0.000324731,0.00007718165,0.00008645022],"category_scores_gemma":[0.007097708,0.0001384245,0.00002736509,0.000108534,0.0002275778,0.0004311734,0.0001554265,0.0003556395,0.000001256066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002247801,"about_ca_system_score_gemma":0.0001014569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007790175,"about_ca_topic_score_gemma":5.344482e-7,"domain_scores_codex":[0.9982171,0.0002875643,0.000628898,0.0002113836,0.0002440876,0.0004109781],"domain_scores_gemma":[0.9843929,0.01434033,0.0004247109,0.0002607482,0.0002523982,0.0003289185],"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":[0.001110722,0.0002524597,0.03426783,0.000493088,0.0002124944,0.00005832375,0.0004811292,0.00000380229,0.0002909953,0.860328,0.003372434,0.09912868],"study_design_scores_gemma":[0.001934081,0.002442641,0.1059202,0.0008970372,0.0006094017,0.0008611981,0.0003531605,0.02861908,0.0000788673,0.8550146,0.002713056,0.0005566445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002391974,0.0003470317,0.9962974,0.0001261855,0.000177205,0.0001280011,0.0003042739,0.00001328637,0.000214656],"genre_scores_gemma":[0.1754114,0.00001346711,0.8242966,0.0000469998,0.0001861856,0.00000387224,0.0000173524,0.00001531874,0.000008881343],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1730194,"threshold_uncertainty_score":0.8497131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.245700184439483,"score_gpt":0.5004273527028614,"score_spread":0.2547271682633784,"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."}}