{"id":"W4283161844","doi":"10.1007/s10985-022-09561-9","title":"Marker-dependent observation and carry-forward of internal covariates in Cox regression","year":2022,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Proportional hazards model; Regression; Carry (investment); Regression analysis; Econometrics; Statistics; Logistic regression; Computer science; Mathematics; Economics","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":[0.09553373,0.001398517,0.002493482,0.001360836,0.001408955,0.002456143,0.005859991,0.00371013,0.003939501],"category_scores_gemma":[0.223989,0.001955739,0.003177325,0.003185998,0.004761815,0.004655092,0.002701842,0.004752786,0.0007392241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175041,"about_ca_system_score_gemma":0.004728912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034483,"about_ca_topic_score_gemma":0.008428382,"domain_scores_codex":[0.965278,0.02317565,0.001710906,0.006128527,0.00231186,0.001395008],"domain_scores_gemma":[0.7707642,0.158868,0.008505296,0.05758011,0.00295706,0.001325439],"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":[0.004784861,0.0005392075,0.1862911,0.001035749,0.003787211,0.002518908,0.003819117,0.06912928,0.002881158,0.5464872,0.006906261,0.17182],"study_design_scores_gemma":[0.0006110487,0.001646937,0.05885636,0.0004896809,0.003384271,0.001976051,0.0005620018,0.3865644,0.00717299,0.5255663,0.01273754,0.0004323797],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06014069,0.001098765,0.9350738,0.0009075062,0.0003344354,0.0001445034,0.001181303,0.0005709265,0.0005480058],"genre_scores_gemma":[0.7777899,0.001530117,0.2022444,0.0005734129,0.000598735,0.001307582,0.002787058,0.0003403696,0.01282855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09553373,"threshold_uncertainty_score":0.5052367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09092513899810067,"score_gpt":0.3792862600246504,"score_spread":0.2883611210265498,"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."}}