{"id":"W4381380443","doi":"10.1093/aje/kwad143","title":"Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring","year":2023,"lang":"en","type":"article","venue":"American Journal of Epidemiology","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo","funders":"National Institute of Allergy and Infectious Diseases; Centers for Disease Control and Prevention; Patient-Centered Outcomes Research Institute; American Heart Association; Harvard University; National Heart, Lung, and Blood Institute; Brigham and Women's Hospital; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Censoring (clinical trials); Parametric statistics; Statistics; Parametric model; Mathematics; Econometrics; Medicine; Computer science; Applied mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1177096,0.001414082,0.002083736,0.002493277,0.0007470143,0.002219364,0.002714186,0.003055419,0.00223949],"category_scores_gemma":[0.4153864,0.0007678194,0.002797396,0.001459368,0.002498549,0.003439503,0.003278164,0.003172478,0.0001951541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001567492,"about_ca_system_score_gemma":0.003190041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004975636,"about_ca_topic_score_gemma":0.003286416,"domain_scores_codex":[0.9389867,0.0524773,0.002448967,0.00197042,0.003463826,0.0006528686],"domain_scores_gemma":[0.4661715,0.5023835,0.008408519,0.01706385,0.005421386,0.000551156],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001228573,0.0002329481,0.03743299,0.001008351,0.001644327,0.001471216,0.00133362,0.6344038,0.002055477,0.1843171,0.003639192,0.1312325],"study_design_scores_gemma":[0.0001853966,0.0004433783,0.003455441,0.0002409606,0.000224902,0.000304762,0.0002220585,0.8448237,0.002621626,0.1456535,0.001747355,0.0000769793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02641794,0.0001788288,0.971594,0.0005263423,0.00004371597,0.0001913617,0.0001457227,0.0003260886,0.0005759633],"genre_scores_gemma":[0.3705918,0.0002331171,0.6264375,0.0005885124,0.00007076701,0.0008926311,0.0005301966,0.0002438152,0.0004116366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8822904,"threshold_uncertainty_score":0.6225152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6095702903568091,"score_gpt":0.5538447234638906,"score_spread":0.05572556689291852,"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."}}