{"id":"W2729708791","doi":"10.1111/biom.12741","title":"Cox Regression with Dependent Error in Covariates","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; ACT Government; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health; Banff International Research Station for Mathematical Innovation and Discovery","keywords":"Covariate; Heteroscedasticity; Statistics; Econometrics; Regression; Regression analysis; Inference; Standard error; Observational error; Errors-in-variables models; Variance (accounting); Proportional hazards model; Nonparametric statistics; Mathematics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0005818275,0.0001005493,0.0001976867,0.0003744155,0.0001253122,0.0001008934,0.000279196,0.00007533611,0.00008506559],"category_scores_gemma":[0.009208877,0.00006384074,0.00001723249,0.0004944591,0.00007598378,0.00006121321,0.00009444981,0.0001056711,0.00001315829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003687429,"about_ca_system_score_gemma":0.00003142596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000986447,"about_ca_topic_score_gemma":0.00002799525,"domain_scores_codex":[0.9991358,0.0000497331,0.00018202,0.0001786198,0.0002695667,0.0001842971],"domain_scores_gemma":[0.9982401,0.000974282,0.0001800849,0.0004627029,0.00007267184,0.00007016312],"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":[0.0002270765,0.0007516038,0.1769566,0.0003081106,0.00004930387,0.0002823538,0.0003476213,0.000001130754,0.002027756,0.6189029,0.002393713,0.1977517],"study_design_scores_gemma":[0.003029861,0.0007086385,0.4202463,0.0005533557,0.00006985956,0.00002852802,0.0001871553,0.004846158,0.00715754,0.5605252,0.001918281,0.0007291157],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3792998,0.0001807832,0.6075445,0.000457375,0.0005188555,0.0004063919,0.00006561578,0.00007455854,0.01145211],"genre_scores_gemma":[0.6163554,0.00001658792,0.3833029,0.00001871613,0.00002246748,0.000007075481,9.476787e-7,0.000009794047,0.0002660489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2432896,"threshold_uncertainty_score":0.999137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.201242634077411,"score_gpt":0.4397560005911356,"score_spread":0.2385133665137246,"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."}}