{"id":"W2071834496","doi":"10.1002/sim.5536","title":"Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Covariate; Pairwise comparison; Computer science; Statistics; Robustness (evolution); Econometrics; Binary data; Data mining; Binary number; Mathematics; Machine learning","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":[0.04002148,0.001715858,0.002216797,0.004062342,0.0009786073,0.001812795,0.00528402,0.002718894,0.005955999],"category_scores_gemma":[0.1346963,0.001237453,0.002944207,0.004129124,0.002676555,0.004883975,0.003309213,0.006445024,0.001788987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323201,"about_ca_system_score_gemma":0.003179206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002693362,"about_ca_topic_score_gemma":0.003597357,"domain_scores_codex":[0.9890814,0.008038207,0.0004946463,0.001209294,0.0009686153,0.0002078773],"domain_scores_gemma":[0.9318978,0.05788461,0.003439171,0.003798699,0.002447224,0.0005325369],"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":[0.0001923302,0.0001627915,0.009157925,0.0007889514,0.0006040957,0.0002630267,0.0008629381,0.149239,0.001191188,0.5625122,0.006069941,0.2689558],"study_design_scores_gemma":[0.00006948256,0.00007990052,0.002090409,0.0001964725,0.0001305198,0.0002833838,0.0001413422,0.3984043,0.0007453503,0.5901602,0.007610675,0.00008790944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009054645,0.0003357942,0.9982192,0.0001923826,0.00002707574,0.00004281992,0.00007575135,0.00006753644,0.000133941],"genre_scores_gemma":[0.04107237,0.00172822,0.9534112,0.0001982404,0.0002641715,0.0008579087,0.0007204579,0.0001686045,0.001578856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04002148,"threshold_uncertainty_score":0.2116563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2687078604948213,"score_gpt":0.5006180560725655,"score_spread":0.2319101955777442,"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."}}