{"id":"W2368506875","doi":"","title":"Adjusted empirical likelihood in Cox proportional hazard model","year":2009,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Proportional hazards model; Hazard ratio; Statistics; Confidence interval; Mathematics; Monte Carlo method; Empirical likelihood; Hazard; Econometrics; Maximum likelihood; Regression analysis; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002707316,0.0001580306,0.0003038217,0.0003029308,0.00005649836,0.0001323492,0.0005438488,0.0001173436,0.001066118],"category_scores_gemma":[0.001142022,0.000110019,0.0001105781,0.0009405701,0.00007173154,0.0003952015,0.00007657403,0.0001844609,0.0005461465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009555648,"about_ca_system_score_gemma":0.000195247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009066539,"about_ca_topic_score_gemma":0.00001918903,"domain_scores_codex":[0.9964646,0.0002678645,0.0007866246,0.0005732901,0.001552735,0.0003548841],"domain_scores_gemma":[0.998776,0.0003314504,0.000106798,0.000423285,0.000171322,0.0001911385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001115988,0.003610692,0.09022973,0.000004499957,0.00002339442,0.0002178998,0.003255784,0.0342313,0.09519852,0.04552373,0.1419451,0.5846434],"study_design_scores_gemma":[0.0008725774,0.0003355144,0.1011126,0.000007931214,0.000003605619,0.00002254712,0.0004056098,0.6800549,0.01120749,0.2048212,0.0008403358,0.0003156739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.438874,0.0001365204,0.3798927,0.006815153,0.0001979807,0.0005326284,0.000006824947,0.0001575103,0.1733867],"genre_scores_gemma":[0.7200148,0.000001616071,0.274904,0.002075908,0.00002699389,0.000008794311,0.000001864969,0.000005941296,0.002960121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6458236,"threshold_uncertainty_score":0.9998471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2642489423522424,"score_gpt":0.4977822513228168,"score_spread":0.2335333089705744,"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."}}