{"id":"W2902433780","doi":"10.1002/sta4.209","title":"Semiparametric estimation for the accelerated failure time model with length‐biased sampling and covariate measurement error","year":2018,"lang":"en","type":"article","venue":"Stat","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariate; Estimator; Inference; Robustness (evolution); Observational error; Statistics; Truncation (statistics); Accelerated failure time model; Censoring (clinical trials); Computer science; Term (time); Statistical inference; Econometrics; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01717947,0.001007255,0.002025828,0.001619742,0.0004588902,0.001473802,0.002836409,0.00148315,0.002552485],"category_scores_gemma":[0.0664537,0.0008806164,0.00221698,0.001675611,0.001689131,0.002177754,0.003319504,0.002624258,0.000465406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008859683,"about_ca_system_score_gemma":0.002061293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00254896,"about_ca_topic_score_gemma":0.002348374,"domain_scores_codex":[0.9931719,0.004889612,0.0002792305,0.0006870448,0.0007157284,0.0002565732],"domain_scores_gemma":[0.9386145,0.04955288,0.005332067,0.004124005,0.001889618,0.0004869606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002113731,0.0001392953,0.01527277,0.0006838893,0.0006012673,0.0005388124,0.000665159,0.5496073,0.002714052,0.3149366,0.00243481,0.1121947],"study_design_scores_gemma":[0.00002528168,0.00006033457,0.001905554,0.00004623635,0.00006619769,0.0001535673,0.00004111899,0.8993918,0.0005024818,0.09675089,0.001024255,0.00003220733],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008900432,0.0001959347,0.9904296,0.0001305665,0.00001209843,0.00002507395,0.00006784219,0.00006448929,0.0001738747],"genre_scores_gemma":[0.559042,0.002124653,0.4317807,0.0003344601,0.0002734375,0.0007970415,0.001221962,0.0001635959,0.004262232],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01717947,"threshold_uncertainty_score":0.09085482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3881950526518411,"score_gpt":0.4202038450159468,"score_spread":0.03200879236410575,"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."}}