{"id":"W4388851130","doi":"10.1002/sim.9963","title":"Two‐stage targeted maximum likelihood estimation for mixed aggregate and individual participant data analysis with an application to multidrug resistant tuberculosis","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Université de Montréal; McGill University; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Aggregate data; Weighting; Computer science; Missing data; Estimation; Tuberculosis; Aggregate (composite); Inverse probability weighting; Meta-analysis; Statistics; Population; Contrast (vision); Medicine; Econometrics; Machine learning; Artificial intelligence; Mathematics; Internal medicine; Estimator; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.05683197,0.001665931,0.003419643,0.003290986,0.0007611655,0.002381462,0.003346439,0.002221515,0.003472104],"category_scores_gemma":[0.1393426,0.001459249,0.006172033,0.003445414,0.001426702,0.00267646,0.003068032,0.004132041,0.0006685357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249513,"about_ca_system_score_gemma":0.003197516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003421226,"about_ca_topic_score_gemma":0.003920983,"domain_scores_codex":[0.951552,0.04284416,0.001230216,0.002413998,0.001668697,0.0002909675],"domain_scores_gemma":[0.8623272,0.1271386,0.003033266,0.005100711,0.001979711,0.0004205283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001034023,0.0002916401,0.01517741,0.003906171,0.010876,0.001100034,0.001033378,0.3061823,0.002680245,0.2600997,0.006060535,0.3915585],"study_design_scores_gemma":[0.00036435,0.000388761,0.002143718,0.0003487865,0.001061546,0.000347824,0.00009550714,0.7111691,0.001093002,0.2741116,0.008767915,0.0001078934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001191242,0.0005169415,0.9975773,0.0002082034,0.00003998531,0.000126196,0.00007926829,0.0001220511,0.0001387987],"genre_scores_gemma":[0.07679463,0.0008154121,0.9187887,0.0005039438,0.0001704702,0.00149387,0.0004056236,0.0001455575,0.000881854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05683197,"threshold_uncertainty_score":0.3005598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1470997536980372,"score_gpt":0.4430485501490196,"score_spread":0.2959487964509824,"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."}}