{"id":"W2103371988","doi":"10.1002/sim.5705","title":"The performance of different propensity score methods for estimating marginal hazard ratios","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":995,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health; Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Propensity score matching; Covariate; Statistics; Odds ratio; Estimator; Matching (statistics); Medicine; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08822343,0.002135994,0.00260438,0.006001812,0.001082461,0.00325052,0.003723077,0.001975625,0.004914273],"category_scores_gemma":[0.2533046,0.001278077,0.005449877,0.007219727,0.001711088,0.004071855,0.002929022,0.003688971,0.001151221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001936039,"about_ca_system_score_gemma":0.003483006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008023952,"about_ca_topic_score_gemma":0.006251296,"domain_scores_codex":[0.9445329,0.0427882,0.003535114,0.003798114,0.004837677,0.0005080369],"domain_scores_gemma":[0.8561339,0.1137137,0.00880006,0.01264468,0.008081779,0.0006259437],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001250341,0.0003003823,0.06174538,0.002009457,0.006442712,0.0002785465,0.001276182,0.2026966,0.001411559,0.1096856,0.01044673,0.6024566],"study_design_scores_gemma":[0.000765769,0.0005923841,0.02605526,0.001010339,0.001514827,0.000585917,0.0003417928,0.788509,0.003236327,0.1551221,0.02180606,0.0004602984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01159006,0.002482056,0.982255,0.0005612064,0.0001496168,0.0007394147,0.0005586121,0.0005693061,0.001094631],"genre_scores_gemma":[0.149355,0.003587149,0.8403804,0.0004836649,0.0002539513,0.002448234,0.001760233,0.0005919473,0.001139475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9117765,"threshold_uncertainty_score":0.4665757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.220207531187187,"score_gpt":0.4904911441960751,"score_spread":0.2702836130088881,"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."}}