{"id":"W2131839274","doi":"10.1002/sim.6621","title":"Terminating observation within matched pairs of subjects in a matched cohort analysis: a Monte Carlo simulation study","year":2015,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; 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","keywords":"Censoring (clinical trials); Monte Carlo method; Statistics; Observational study; Cohort; Event (particle physics); Computer science; Cohort study; Econometrics; Mathematics; Physics","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":[0.1235493,0.0008699205,0.002129161,0.001283804,0.001356275,0.002285546,0.002533654,0.002599411,0.002682764],"category_scores_gemma":[0.2662152,0.0008396019,0.003410679,0.001668142,0.001932219,0.002842933,0.002421291,0.00382464,0.0002962041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002220924,"about_ca_system_score_gemma":0.003809339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009209069,"about_ca_topic_score_gemma":0.006097357,"domain_scores_codex":[0.944,0.04919599,0.001425788,0.002977881,0.001588843,0.0008113892],"domain_scores_gemma":[0.5119408,0.4591204,0.009009577,0.01454039,0.003891311,0.001497522],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01592652,0.005305681,0.1935002,0.001065952,0.007052807,0.00256752,0.003538935,0.4415502,0.001521581,0.2249589,0.002925239,0.1000865],"study_design_scores_gemma":[0.001118075,0.001702424,0.007312845,0.0002509212,0.001653427,0.0003566211,0.0004187892,0.9263402,0.0008893462,0.05806046,0.001773366,0.0001235019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5243766,0.001721503,0.46762,0.001310294,0.0001911305,0.001742384,0.0003875529,0.0001205999,0.002530053],"genre_scores_gemma":[0.8796818,0.0005348828,0.1170895,0.0004103303,0.00006258757,0.00135559,0.0002744533,0.0000313916,0.0005594341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8764507,"threshold_uncertainty_score":0.653399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.172668364087971,"score_gpt":0.4504295781613126,"score_spread":0.2777612140733416,"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."}}