{"id":"W2157629737","doi":"10.1002/cjs.10033","title":"Efficient estimation for the proportional hazards model with competing risks and current status data","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Consistency (knowledge bases); Statistics; Econometrics; Regression analysis; Statistical model; Proportional hazards model; Regression; Mathematics; Estimation; Current (fluid); Maximum likelihood; Computer science; Economics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003991196,0.00009440355,0.0001384988,0.00006043233,0.0003352029,0.00008916183,0.0001690113,0.00002230559,0.0000229544],"category_scores_gemma":[0.001676684,0.0000647111,0.00001443703,0.00009212874,0.0001400645,0.00004579876,0.000008227864,0.0001492521,0.000001026644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009130883,"about_ca_system_score_gemma":0.001154987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005635783,"about_ca_topic_score_gemma":0.000447182,"domain_scores_codex":[0.9990511,0.00001886113,0.0003904142,0.0001105688,0.000223864,0.0002051848],"domain_scores_gemma":[0.9979963,0.000652462,0.0003198357,0.0001915407,0.0005136665,0.0003261816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001622135,0.000038275,0.00007075509,0.00003577618,0.00001850279,0.000002795848,0.0001438059,0.03821736,0.000001815498,0.8746991,0.01092241,0.07583324],"study_design_scores_gemma":[0.0003472056,0.00005998465,0.005096137,0.00004702975,0.0001144053,0.00003083669,0.00007811173,0.8846242,0.00000167894,0.1084303,0.001093071,0.00007699721],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002144025,0.000148747,0.9892156,0.001124975,0.0000481868,0.0002891899,0.00697914,0.000005820246,0.00004428085],"genre_scores_gemma":[0.5535378,0.00001103016,0.4461175,0.00005952659,0.00003390336,0.000005215293,0.0002205289,0.000006950476,0.000007520778],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8464069,"threshold_uncertainty_score":0.2638842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.214664734283162,"score_gpt":0.3933112959100772,"score_spread":0.1786465616269151,"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."}}