{"id":"W1979475075","doi":"10.1097/ede.0b013e31815be045","title":"The Breslow Estimator of the Nonparametric Baseline Survivor Function in Cox’s Regression Model","year":2008,"lang":"en","type":"article","venue":"Epidemiology","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Estimator; Nonparametric statistics; Statistics; Proportional hazards model; Mathematics; Econometrics; Regression analysis; Estimating equations; Regression; Baseline (sea); Nonparametric regression; Function (biology); Applied mathematics","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":[0.01157334,0.0005322258,0.000949393,0.001667643,0.0005802264,0.001423913,0.001733676,0.001319601,0.005958543],"category_scores_gemma":[0.04207567,0.0003980861,0.0007800582,0.002695117,0.00154206,0.003164435,0.001459597,0.003443352,0.00258373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008020282,"about_ca_system_score_gemma":0.001966475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003029354,"about_ca_topic_score_gemma":0.002384127,"domain_scores_codex":[0.9950256,0.00328564,0.0001470607,0.000412802,0.0009192494,0.0002095998],"domain_scores_gemma":[0.9920845,0.005278542,0.0007118138,0.001148445,0.0006705942,0.0001060846],"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":[0.000075139,0.00002855155,0.003151081,0.000195769,0.00006163531,0.00009602371,0.0001677903,0.008743456,0.0004980512,0.8418782,0.02058532,0.1245189],"study_design_scores_gemma":[0.0000425394,0.0001090241,0.003473783,0.0002339768,0.00005999262,0.0005034538,0.0001187689,0.05314834,0.001021092,0.8866161,0.05459039,0.00008248453],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002245092,0.002192688,0.9886904,0.001494472,0.0002782089,0.0000437421,0.0003825413,0.0002097507,0.004463043],"genre_scores_gemma":[0.2130615,0.008220045,0.7470047,0.00236894,0.001816162,0.0009042381,0.002394565,0.000557078,0.02367271],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01157334,"threshold_uncertainty_score":0.0612064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2281356613513611,"score_gpt":0.4223525319071024,"score_spread":0.1942168705557414,"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."}}