{"id":"W4234921364","doi":"10.1002/sim.2781","title":"The performance of different propensity score methods for estimating marginal odds ratios","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":244,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto","funders":"Canadian Institutes of Health Research; Institute for Clinical Evaluative Sciences","keywords":"Propensity score matching; Covariate; Statistics; Odds ratio; Odds; Estimator; Matching (statistics); Medicine; Mathematics; Logistic regression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001440583,0.0001533055,0.0003956063,0.00005870846,0.0001196971,0.00000786383,0.0001879101,0.00004498881,0.00001675221],"category_scores_gemma":[0.003346611,0.00009087888,0.00001741449,0.0001144231,0.0003333985,0.00004524119,0.00004891106,0.0001915706,2.69258e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007043953,"about_ca_system_score_gemma":0.00003465485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000300062,"about_ca_topic_score_gemma":0.00008847591,"domain_scores_codex":[0.998661,0.0001105302,0.0006178968,0.0001558654,0.0002195026,0.0002352355],"domain_scores_gemma":[0.9954485,0.003698763,0.000315655,0.0002784863,0.0002315271,0.00002707009],"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":[0.00009186043,0.00007759775,0.004458784,0.0007301857,0.0000139113,0.000002843605,0.000260542,0.00006668358,0.006183057,0.9101394,0.003094376,0.0748807],"study_design_scores_gemma":[0.0005251256,0.0005000319,0.004005472,0.0004443988,0.00004303695,0.000004917463,0.00005520105,0.1104467,0.01335285,0.8703821,0.0001108719,0.0001293549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05286114,0.00005312101,0.9456895,0.0001375669,0.0001436716,0.0007104615,0.0000210877,0.00004277977,0.0003406586],"genre_scores_gemma":[0.1438901,0.00002468122,0.8556389,0.00001751047,0.000103859,0.0001050498,0.00002077667,0.00001821366,0.0001808416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.11038,"threshold_uncertainty_score":0.4006448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1636371954417831,"score_gpt":0.4737266236584863,"score_spread":0.3100894282167033,"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."}}