{"id":"W3026963084","doi":"10.1093/biostatistics/kxaa020","title":"Causal inference for recurrent event data using pseudo-observations","year":2020,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; HEC Montréal; Jewish General Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Institut de Valorisation des Données","keywords":"Estimator; Inverse probability weighting; Statistics; Inverse probability; Mathematics; Weighting; Regression; Event (particle physics); Regression analysis; Computer science; Econometrics; Bayesian probability; Medicine","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":[0.0503354,0.0009010723,0.001638335,0.002584238,0.000769216,0.001985697,0.003230982,0.001755641,0.00348665],"category_scores_gemma":[0.2365011,0.000637649,0.002119344,0.002493159,0.002374492,0.003336384,0.002339395,0.002462939,0.0003675364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007411905,"about_ca_system_score_gemma":0.001061184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001985456,"about_ca_topic_score_gemma":0.001367694,"domain_scores_codex":[0.9744124,0.01926254,0.001098416,0.002867492,0.002029104,0.00033018],"domain_scores_gemma":[0.7524452,0.2088338,0.01460597,0.01931163,0.004203004,0.0006003196],"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":[0.001381979,0.0004259954,0.1150951,0.001241676,0.001999536,0.001224666,0.00154792,0.2154859,0.002188979,0.3443228,0.002852511,0.3122329],"study_design_scores_gemma":[0.0001422053,0.0004758841,0.02013971,0.0001693773,0.0003054976,0.0004689264,0.0002311869,0.7082088,0.002215557,0.263731,0.003809089,0.0001027202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02920603,0.000332011,0.9692616,0.0001391692,0.00005830904,0.0001332309,0.0003316431,0.0002020906,0.0003360278],"genre_scores_gemma":[0.6874553,0.0006003022,0.3074983,0.000267153,0.0001564571,0.0009535627,0.001936343,0.0001043985,0.001028061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0503354,"threshold_uncertainty_score":0.2662022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5969398403880808,"score_gpt":0.4979805900281105,"score_spread":0.09895925035997033,"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."}}