{"id":"W4307835686","doi":"10.1177/09622802221134172","title":"Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations","year":2022,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research; St. Michael's Hospital; University of Waterloo; University of Toronto","funders":"","keywords":"Covariate; Laplace's method; Markov chain Monte Carlo; Bayesian probability; Inference; Parametric statistics; Proportional hazards model; Computer science; Bayesian inference; Mathematics; Econometrics; Applied mathematics; Statistics; Algorithm; Artificial intelligence","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01595795,0.0002743684,0.0007087305,0.0002724712,0.0006394318,0.00008883498,0.0004670142,0.0002137229,0.001319659],"category_scores_gemma":[0.09894222,0.0002209639,0.00004634516,0.001003766,0.001091723,0.0001043831,0.0004906341,0.002161196,0.000002432529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001652551,"about_ca_system_score_gemma":0.001297338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001044223,"about_ca_topic_score_gemma":0.00004393946,"domain_scores_codex":[0.9888908,0.005914003,0.0009103018,0.00081499,0.002459374,0.001010592],"domain_scores_gemma":[0.9109954,0.08726742,0.0001289626,0.0003832177,0.0004909001,0.0007341215],"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":[0.0005383936,0.0004296805,0.0005410158,0.0004005993,0.00004206258,0.0001811088,0.0002136442,0.00002223544,0.00005753021,0.8422109,0.0006466666,0.1547162],"study_design_scores_gemma":[0.001060382,0.0008089849,0.0007885692,0.000109624,0.00002815027,0.0000211286,0.00008371235,0.399404,0.00003384782,0.5969443,0.0005427527,0.0001745017],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006749607,0.00005202186,0.9949412,0.001657468,0.0001824668,0.001579404,0.0004785304,0.0000580939,0.0003758426],"genre_scores_gemma":[0.013588,0.00002820086,0.984054,0.0002389596,0.00009154304,0.001796048,0.00007964535,0.00005235186,0.00007130436],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3993818,"threshold_uncertainty_score":0.9995933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2349063491478718,"score_gpt":0.5408256184953694,"score_spread":0.3059192693474976,"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."}}