{"id":"W2113376504","doi":"10.1002/0471667196.ess7125","title":"SemiParametric Analysis of Competing Risks Data","year":2010,"lang":"en","type":"other","venue":"Encyclopedia of Statistical Sciences","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Covariate; Hazard; Cumulative incidence; Confidence interval; Hazard ratio; Econometrics; Proportional hazards model; Cumulative risk; Statistics; Computer science; Medicine; Mathematics; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01786936,0.001305283,0.002393307,0.003318232,0.0005280434,0.002847737,0.002742843,0.001382495,0.007981581],"category_scores_gemma":[0.06831559,0.0009790657,0.002579883,0.002261688,0.00185395,0.002298334,0.003447834,0.0034298,0.00104597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001268273,"about_ca_system_score_gemma":0.001525849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002186298,"about_ca_topic_score_gemma":0.001425161,"domain_scores_codex":[0.987933,0.008469652,0.0005199994,0.001203785,0.001475915,0.0003977453],"domain_scores_gemma":[0.8719613,0.1130027,0.005228851,0.005410326,0.003426059,0.0009707135],"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":[0.0002507352,0.0001666978,0.01070122,0.0007970658,0.0006366721,0.0007711246,0.0005728416,0.3152256,0.001822789,0.5909431,0.004715108,0.07339713],"study_design_scores_gemma":[0.00002049679,0.00007311733,0.001629234,0.00006940219,0.00004985921,0.0001778154,0.00005136622,0.7545985,0.0004039338,0.2410511,0.001835629,0.00003957085],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008600329,0.0005177152,0.9888178,0.0002940185,0.00002539896,0.0000738009,0.0005238528,0.0001928386,0.0009542644],"genre_scores_gemma":[0.6179929,0.002325955,0.3635442,0.0005599589,0.0004479206,0.001378157,0.004236661,0.0004129993,0.009101218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01786936,"threshold_uncertainty_score":0.09450328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1804774038184393,"score_gpt":0.4548967747367608,"score_spread":0.2744193709183215,"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."}}