{"id":"W4400969532","doi":"10.1177/09622802241262527","title":"Minimizing confounding in comparative observational studies with time-to-event outcomes: An extensive comparison of covariate balancing methods using Monte Carlo simulation","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; University of Toronto","funders":"","keywords":"Covariate; Confounding; Observational study; Monte Carlo method; Econometrics; Computer science; Statistics; Event (particle physics); Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02345545,0.0002781973,0.001399296,0.0007361455,0.000105597,0.00005848044,0.0003227092,0.0001919856,0.0002428368],"category_scores_gemma":[0.0896238,0.0002150752,0.00004527744,0.001435109,0.0006708459,0.0002435648,0.0002816099,0.001445916,0.000004774496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007584707,"about_ca_system_score_gemma":0.0005348758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001549696,"about_ca_topic_score_gemma":0.0001196934,"domain_scores_codex":[0.9878798,0.007413037,0.001437758,0.000660994,0.001918055,0.0006903529],"domain_scores_gemma":[0.8763608,0.1218766,0.0001471072,0.0003257093,0.0009762797,0.0003135602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001277386,0.001178969,0.008035371,0.002950575,0.0006931156,0.0007482059,0.04902679,0.1011233,0.01519155,0.5037282,0.000329819,0.3157167],"study_design_scores_gemma":[0.0003055575,0.0003791059,0.001750444,0.001946688,0.00002856903,0.00000330644,0.004200424,0.8138084,0.00098916,0.176333,0.00005748111,0.0001978877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04869295,0.0002483684,0.9496502,0.000245134,0.00008637451,0.000836041,0.00002831294,0.00007419018,0.0001384688],"genre_scores_gemma":[0.254606,0.000006296901,0.7451642,0.00004155295,0.00002871487,0.00008815196,0.00000467391,0.00003019081,0.00003027527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.712685,"threshold_uncertainty_score":0.9180447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8432242746118068,"score_gpt":0.7654410243786494,"score_spread":0.07778325023315746,"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."}}