{"id":"W2087811140","doi":"10.1002/sim.3830","title":"A copula‐based mixed Poisson model for bivariate recurrent events under event‐dependent censoring","year":2010,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; GlaxoSmithKline","keywords":"Bivariate analysis; Copula (linguistics); Censoring (clinical trials); Estimator; Econometrics; Poisson distribution; Statistics; Marginal model; Random effects model; Marginal distribution; Negative binomial distribution; Parametric statistics; Mathematics; Regression analysis; Random variable; Medicine; Internal medicine; Meta-analysis","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001673205,0.0002716816,0.0005546262,0.0001669298,0.00009007124,0.00001251844,0.0002561322,0.000136146,0.0002710623],"category_scores_gemma":[0.01040329,0.0002241373,0.00004073839,0.000163468,0.0001183096,0.00002990146,0.00005140168,0.0005314773,0.000006771878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009266226,"about_ca_system_score_gemma":0.000112141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008007144,"about_ca_topic_score_gemma":0.0003537858,"domain_scores_codex":[0.9976195,0.000136977,0.00080059,0.00041002,0.0005469306,0.0004859624],"domain_scores_gemma":[0.9953167,0.003575206,0.0002467209,0.0004183635,0.0002422704,0.0002006842],"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":[0.0001407056,0.0002482841,0.0002187638,0.0003411558,0.00002551729,0.0000109726,0.0003780384,0.0002981822,0.001653509,0.9783562,0.002357973,0.01597065],"study_design_scores_gemma":[0.001596797,0.0001635015,0.0008933373,0.0001926222,0.00005972751,0.000001700932,0.00006131391,0.4086372,0.0001186602,0.5880549,0.00005825998,0.0001619802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009290518,0.00002113999,0.986948,0.0005632183,0.00154695,0.0006493485,0.0006986135,0.00004268552,0.000239504],"genre_scores_gemma":[0.3431495,0.0000107865,0.6561468,0.000149418,0.0001373832,0.00009017874,0.00005826309,0.00003751824,0.0002202004],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.408339,"threshold_uncertainty_score":0.9979325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1449655822639636,"score_gpt":0.4545331163864958,"score_spread":0.3095675341225322,"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."}}