{"id":"W2969810107","doi":"10.1007/s10985-019-09482-0","title":"Semiparametric methods for survival data with measurement error under additive hazards cure rate models","year":2019,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Proportional hazards model; Statistics; Observational error; Econometrics; Flexibility (engineering); Population; Fraction (chemistry); Computer science; Additive model; Estimation; Mathematics; Medicine; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008691306,0.0003639657,0.001149084,0.0004102309,0.0001121403,0.0001511263,0.002060486,0.000135046,0.001028777],"category_scores_gemma":[0.006137232,0.00026742,0.0001401437,0.002313684,0.00008919254,0.0005247402,0.0009290371,0.0002522258,0.00004695015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007549923,"about_ca_system_score_gemma":0.0002246163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001317125,"about_ca_topic_score_gemma":0.0001397223,"domain_scores_codex":[0.9955969,0.001136466,0.0006335978,0.001346682,0.0008016763,0.0004847027],"domain_scores_gemma":[0.9873992,0.0063885,0.0003754796,0.005019732,0.0006044097,0.0002127309],"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":[0.001629995,0.002210192,0.001955803,0.001228106,0.06082358,0.00001949295,0.0004986611,0.01062959,0.000930789,0.4714991,0.1159544,0.3326203],"study_design_scores_gemma":[0.000644055,0.0001248791,0.0003807135,0.00004785928,0.007638284,9.638693e-7,0.0002073152,0.9115673,0.0001064431,0.07343938,0.005381958,0.000460793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001601187,0.0001357528,0.9789547,0.0003171985,0.0001026625,0.0006203827,0.01815853,0.0000621033,0.001488565],"genre_scores_gemma":[0.005184473,0.00003793084,0.9874971,0.0001972251,0.0000808956,0.00004861359,0.00626221,0.00005122085,0.0006403325],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9009377,"threshold_uncertainty_score":0.9999778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2946502528050171,"score_gpt":0.4639656007323654,"score_spread":0.1693153479273483,"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."}}