{"id":"W2264767503","doi":"10.1161/circulationaha.115.017719","title":"Introduction to the Analysis of Survival Data in the Presence of Competing Risks","year":2016,"lang":"en","type":"article","venue":"Circulation","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2536,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Covariate; Medicine; Proportional hazards model; Survival analysis; Cumulative incidence; Incidence (geometry); Survival function; Event (particle physics); Statistics; Hazard ratio; Outcome (game theory); Hazard; Demography; Accelerated failure time model; Econometrics; Confidence interval; Internal medicine; Cohort; Mathematics","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":[0.03189606,0.001869967,0.001718444,0.003986382,0.0008456346,0.003089857,0.003653634,0.003057933,0.0267117],"category_scores_gemma":[0.09730981,0.001524248,0.004224772,0.00630443,0.00373912,0.002975173,0.003005893,0.01098289,0.00729861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001336205,"about_ca_system_score_gemma":0.003676947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002594965,"about_ca_topic_score_gemma":0.002237865,"domain_scores_codex":[0.9630908,0.02883231,0.001978118,0.001864233,0.003928177,0.0003062704],"domain_scores_gemma":[0.7887508,0.1962314,0.004274825,0.006289776,0.003561327,0.0008918426],"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":[0.0001900003,0.0001815747,0.004430406,0.004368296,0.0007682825,0.001165595,0.001293277,0.01867008,0.00164295,0.5426522,0.09931553,0.3253218],"study_design_scores_gemma":[0.0001265042,0.0002474451,0.003533118,0.001977228,0.0001948417,0.002198645,0.0001788563,0.05276324,0.0007437778,0.6568273,0.2810144,0.0001945532],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004863753,0.004737605,0.9862852,0.002550392,0.0005880888,0.0002748402,0.001176654,0.000993361,0.002907445],"genre_scores_gemma":[0.01207006,0.008027072,0.9697362,0.001822015,0.0024321,0.002063712,0.001161794,0.0006487923,0.00203818],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03189606,"threshold_uncertainty_score":0.1686845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2664686297936992,"score_gpt":0.4426291158539977,"score_spread":0.1761604860602985,"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."}}