{"id":"W1968761139","doi":"10.1002/cjs.5550340408","title":"Multiple imputation methods for recurrent event data with missing event category","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Amgen","keywords":"Imputation (statistics); Missing data; Covariate; Estimator; Event (particle physics); Statistics; Computer science; Event data; Econometrics; Mathematics; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001464754,0.0001731801,0.0003621499,0.000164702,0.0001755768,0.0001012752,0.0003335863,0.00006207385,0.00006287895],"category_scores_gemma":[0.004369419,0.000140074,0.00004238828,0.0001294871,0.0001060268,0.0001210154,0.00001692722,0.0002097592,0.000001171169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000201827,"about_ca_system_score_gemma":0.001441451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00147027,"about_ca_topic_score_gemma":0.01203405,"domain_scores_codex":[0.9982755,0.0002278903,0.000746685,0.0001970425,0.0001917476,0.0003611431],"domain_scores_gemma":[0.9948191,0.003319265,0.0005719262,0.0002985903,0.000564737,0.0004263723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008148923,0.00005985412,0.0004708726,0.0002221356,0.00006854342,0.00009745698,0.0001960888,0.00006392044,0.00006207293,0.3024606,0.02494535,0.6712716],"study_design_scores_gemma":[0.0007522914,0.0004638792,0.001661185,0.0001998483,0.0002343662,0.000134473,0.0001181937,0.04032642,0.0001427976,0.9466481,0.009079988,0.0002384544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001712051,0.0002865292,0.9968572,0.0002457221,0.0004019312,0.000245383,0.001702489,0.000005848305,0.00008371169],"genre_scores_gemma":[0.02565384,0.000006268074,0.9739143,0.00004025432,0.0002038356,0.000004921755,0.0001106414,0.00003287923,0.00003302552],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6710331,"threshold_uncertainty_score":0.6715283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100363950836631,"score_gpt":0.4141410349214595,"score_spread":0.3137770840848284,"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."}}