{"id":"W2124332133","doi":"10.1111/j.1541-0420.2011.01696.x","title":"Empirical Likelihood for Cumulative Hazard Ratio Estimation with Covariate Adjustment","year":2011,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Queen's University","funders":"","keywords":"Covariate; Estimator; Hazard ratio; Statistics; Nonparametric statistics; Confidence interval; Proportional hazards model; Hazard; Econometrics; Parametric statistics; Statistic; Empirical likelihood; 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":[0.0346594,0.001230228,0.002075855,0.002874557,0.000574005,0.002184305,0.003111133,0.002219152,0.003557845],"category_scores_gemma":[0.1825638,0.0009211756,0.001577775,0.002960998,0.002924338,0.003258335,0.003015454,0.004421177,0.001150612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271289,"about_ca_system_score_gemma":0.001834359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001898951,"about_ca_topic_score_gemma":0.0009041396,"domain_scores_codex":[0.9798638,0.01669736,0.0005547121,0.00124915,0.001363723,0.0002711801],"domain_scores_gemma":[0.8842762,0.1044325,0.003536769,0.005319038,0.002125181,0.0003104121],"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":[0.0003313687,0.0001508968,0.01452798,0.0006199715,0.0004734674,0.0005505559,0.0005814442,0.2094951,0.00147698,0.5401357,0.004109729,0.2275469],"study_design_scores_gemma":[0.00009529186,0.0001236562,0.002206194,0.0001147896,0.00006009409,0.0003338274,0.00007046028,0.7116587,0.0009018035,0.2796478,0.004721522,0.0000657923],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001451404,0.0001990078,0.9977279,0.0001299566,0.00001387187,0.00004391729,0.00004945874,0.0001293547,0.0002550848],"genre_scores_gemma":[0.1989589,0.001194051,0.7942812,0.0002828172,0.0003093299,0.00142393,0.0008940555,0.0003316968,0.002324056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0346594,"threshold_uncertainty_score":0.1832986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3266414016709798,"score_gpt":0.4311705265947319,"score_spread":0.1045291249237521,"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."}}