{"id":"W2091956653","doi":"10.2202/1557-4679.1106","title":"Missing Confounding Data in Marginal Structural Models: A Comparison of Inverse Probability Weighting and Multiple Imputation","year":2008,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McGill University","funders":"","keywords":"Inverse probability weighting; Missing data; Inverse probability; Imputation (statistics); Marginal structural model; Statistics; Weighting; Confounding; Censoring (clinical trials); Computer science; Econometrics; Mathematics; Inverse distance weighting; Estimator; Data mining; Posterior probability; Bayesian probability; Medicine","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":[0.0007023558,0.00009364061,0.0002310367,0.0001147837,0.0000545276,0.00002566484,0.000460314,0.00003491089,0.000005866494],"category_scores_gemma":[0.002320204,0.00006922275,0.000020573,0.00006788277,0.0002014211,0.0003893088,0.000169483,0.0002338491,1.240581e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133732,"about_ca_system_score_gemma":0.00009954568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000620615,"about_ca_topic_score_gemma":0.00007625022,"domain_scores_codex":[0.9985723,0.00008013775,0.0007234146,0.00009982578,0.0004270475,0.00009728432],"domain_scores_gemma":[0.9971243,0.001429561,0.0008296272,0.0001590106,0.0004239461,0.00003354713],"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":[0.002183935,0.0007595311,0.3409175,0.0006130097,0.0006968486,0.0004340625,0.04037927,0.01565392,0.03560316,0.4770315,0.002278042,0.08344922],"study_design_scores_gemma":[0.0004613806,0.0000605444,0.002022831,0.0001663342,0.00002030423,0.000234946,0.0005158538,0.419043,0.002004775,0.5753856,0.00001184939,0.00007256208],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7083291,0.00004674148,0.2911252,0.0001955185,0.000102179,0.00009173891,0.00006299122,0.000008061272,0.00003841496],"genre_scores_gemma":[0.6903223,0.00002530411,0.3095847,0.00001446285,0.0000357886,2.692938e-7,0.000009617639,0.000005809595,0.000001757586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4033891,"threshold_uncertainty_score":0.2822822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.333582937149588,"score_gpt":0.4591081692735752,"score_spread":0.1255252321239871,"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."}}