{"id":"W2054869597","doi":"10.1002/sim.3808","title":"Threshold regression for survival data with time‐varying covariates","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Eye Institute; National Heart, Lung, and Blood Institute; National Institute for Occupational Safety and Health; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Covariate; Proportional hazards model; Statistics; Econometrics; Markov chain; Regression analysis; Event (particle physics); Computer science; Inference; Accelerated failure time model; Regression; Markov model; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02378534,0.0009271355,0.00193823,0.002427546,0.0007150068,0.001990036,0.003173111,0.002322386,0.005547059],"category_scores_gemma":[0.08354782,0.0007776538,0.002402518,0.004992296,0.002057445,0.003735585,0.002348865,0.005654978,0.001378391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774743,"about_ca_system_score_gemma":0.002185794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006480178,"about_ca_topic_score_gemma":0.003386324,"domain_scores_codex":[0.9884333,0.007229745,0.0006139027,0.001892112,0.001369403,0.0004614942],"domain_scores_gemma":[0.9471039,0.04394305,0.002998035,0.004104502,0.00146839,0.0003820655],"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":[0.000229595,0.00009956588,0.01088682,0.000714321,0.0003770841,0.0006869338,0.0005744694,0.1800284,0.002654793,0.6803948,0.005724277,0.117629],"study_design_scores_gemma":[0.00004655953,0.0001117464,0.002541741,0.000119446,0.00006130907,0.0002817427,0.00008165112,0.6639,0.0007184905,0.3247609,0.007324636,0.00005177938],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004845391,0.0007862254,0.9922713,0.0004543867,0.00007151929,0.00007704135,0.0003639079,0.0003499691,0.0007803641],"genre_scores_gemma":[0.4070135,0.005562778,0.5685623,0.001050116,0.0007002174,0.00157905,0.003821149,0.0007249207,0.01098598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02378534,"threshold_uncertainty_score":0.1257904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1376283244369878,"score_gpt":0.4428136105549579,"score_spread":0.30518528611797,"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."}}