{"id":"W2896981660","doi":"10.1002/sim.8008","title":"Propensity‐score matching with competing risks in survival analysis","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institute for Work & Health; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Heart and Stroke Foundation of Canada","keywords":"Propensity score matching; Statistics; Covariate; Observational study; Matching (statistics); Hazard ratio; Confounding; Sample size determination; Medicine; Absolute risk reduction; Confidence interval; Econometrics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001356391,0.0001828698,0.0006399223,0.0004041283,0.00005302934,0.00001106962,0.0001833503,0.00005705481,0.0001815781],"category_scores_gemma":[0.001528044,0.0001342208,0.00001328933,0.0009958131,0.0003917107,0.00006452746,0.00007041612,0.0003785256,0.00000458181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082927,"about_ca_system_score_gemma":0.00003677035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001285666,"about_ca_topic_score_gemma":0.01293503,"domain_scores_codex":[0.9983619,0.000150369,0.0005061423,0.0002815475,0.000396811,0.0003031802],"domain_scores_gemma":[0.9980394,0.001155309,0.0002106536,0.000337415,0.0001971812,0.00006005298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001646355,0.0001549616,0.594779,0.0002350136,0.0001877082,0.000470114,0.007737148,0.0001266187,0.00086593,0.3898057,0.0003330047,0.005140123],"study_design_scores_gemma":[0.001050162,0.000599377,0.1665393,0.0008578853,0.0002909161,0.000009614103,0.0020042,0.002799703,0.0004367973,0.8250068,0.00003797758,0.0003672669],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3842071,0.000008850707,0.6120987,0.00008011199,0.0000445546,0.0002167269,0.00001741795,0.00007107168,0.003255508],"genre_scores_gemma":[0.679157,0.00001142733,0.3206189,0.00005121932,0.00007424379,0.00001163854,0.00002110249,0.00001913894,0.0000353833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4352011,"threshold_uncertainty_score":0.7218051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2384664040620321,"score_gpt":0.4583996193977982,"score_spread":0.2199332153357661,"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."}}