{"id":"W3157155409","doi":"10.1002/sim.8997","title":"Using generalized linear models to implement g‐estimation for survival data with time‐varying confounding","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIHR Cambridge Biomedical Research Centre; Bijzonder Onderzoeksfonds UGent; Medical Research Council Canada; Department of Health and Social Care; Medical Research Council; National Institute for Health and Care Research; UK Research and Innovation","keywords":"Confounding; Computer science; Estimation; Weighting; Statistics; Marginal structural model; Software; Proportional hazards model; Econometrics; Observational study; Data mining; Mathematics; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04401878,0.0024652,0.002043859,0.003617665,0.0008171526,0.002665415,0.003941889,0.002972892,0.01738973],"category_scores_gemma":[0.164697,0.001864497,0.005279598,0.005416198,0.002069134,0.003000421,0.004510716,0.006584356,0.005658387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646329,"about_ca_system_score_gemma":0.004066994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0104175,"about_ca_topic_score_gemma":0.009737975,"domain_scores_codex":[0.9652333,0.0295912,0.001293285,0.001728953,0.001788106,0.000365144],"domain_scores_gemma":[0.9006793,0.08670541,0.003264235,0.007021632,0.002081049,0.0002482933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003035141,0.0002054369,0.01041327,0.001923906,0.002623977,0.0008662246,0.001901952,0.199331,0.001519372,0.3626768,0.03099726,0.3872372],"study_design_scores_gemma":[0.0002297592,0.0001960012,0.002441273,0.0004578301,0.0003436176,0.0003215231,0.0002350393,0.4234458,0.001639402,0.5324415,0.03809841,0.0001498269],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006032475,0.0000886995,0.9972356,0.0002488749,0.00005256234,0.00009494022,0.000224964,0.001141996,0.0003091031],"genre_scores_gemma":[0.02111107,0.0003984038,0.9737772,0.0003823502,0.00008373134,0.00153513,0.0007249341,0.0008766915,0.001110448],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04401878,"threshold_uncertainty_score":0.2327963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4354915215451955,"score_gpt":0.5352801309584877,"score_spread":0.0997886094132922,"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."}}