{"id":"W2378268168","doi":"10.1002/sim.6979","title":"A flexible parametric approach for estimating continuous‐time inverse probability of treatment and censoring weights","year":2016,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cancer Care Ontario; Public Health Ontario; University of Toronto","funders":"","keywords":"Inverse probability; Censoring (clinical trials); Marginal structural model; Inverse probability weighting; Parametric statistics; Statistics; Weighting; Event (particle physics); Computer science; Marginal distribution; Proportional hazards model; Confounding; Parametric model; Econometrics; Mathematics; Posterior probability; Medicine; Estimator; Random variable","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006184448,0.0001669991,0.0005381357,0.0001700442,0.00003162282,0.000003296261,0.00007980157,0.00006134546,0.00002568682],"category_scores_gemma":[0.006214996,0.00009969794,0.00001758955,0.0001696436,0.0003145472,0.00005647582,0.00003068544,0.00005826934,5.738456e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002059525,"about_ca_system_score_gemma":0.0000388036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004468844,"about_ca_topic_score_gemma":0.000007684099,"domain_scores_codex":[0.9988214,0.00005540232,0.0005085285,0.0002581076,0.0001309219,0.0002255857],"domain_scores_gemma":[0.99594,0.003359522,0.0002384024,0.0002563294,0.0001400547,0.00006567089],"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":[0.0001919683,0.0005196835,0.007569843,0.001721179,0.00009150749,0.000007592352,0.002018473,0.00003412476,0.008836021,0.8458502,0.001344097,0.1318153],"study_design_scores_gemma":[0.001645746,0.001088224,0.0001315379,0.000404662,0.00006401708,0.000004453575,0.00009328488,0.016926,0.003890424,0.9755436,0.00006697378,0.0001410766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03061324,0.00003031541,0.9674354,0.00004777326,0.00003131436,0.0009755223,0.0001159664,0.00008133832,0.0006691497],"genre_scores_gemma":[0.04404079,0.00003456295,0.955419,0.000007991401,0.00004259875,0.0001542355,0.00001124385,0.00001938531,0.0002702077],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1316742,"threshold_uncertainty_score":0.744038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.137728683128249,"score_gpt":0.4098909007543837,"score_spread":0.2721622176261347,"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."}}