{"id":"W2137538399","doi":"10.1002/sim.5522","title":"Event‐weighted proportional hazards modelling for recurrent gap time data","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Guelph","funders":"","keywords":"Resampling; Proportional hazards model; Statistics; Independence (probability theory); Computer science; Event (particle physics); Marginal model; Econometrics; Mathematics; Inverse; Applied mathematics; Regression analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04365297,0.001165358,0.002072401,0.001796165,0.0006048881,0.001467869,0.005799917,0.002213167,0.004728214],"category_scores_gemma":[0.08624317,0.0007509732,0.002896813,0.002247368,0.001866179,0.002394219,0.003075517,0.004136947,0.0007330239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014201,"about_ca_system_score_gemma":0.001396118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002692185,"about_ca_topic_score_gemma":0.001826849,"domain_scores_codex":[0.9830573,0.01230645,0.0006404716,0.001977263,0.001472679,0.0005458551],"domain_scores_gemma":[0.9499885,0.03920311,0.003550152,0.005610943,0.001256288,0.0003908914],"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":[0.002138674,0.0003667937,0.02735433,0.000906776,0.001662523,0.001221424,0.002048592,0.3649664,0.003096651,0.398961,0.004368614,0.1929082],"study_design_scores_gemma":[0.0001332678,0.0003783491,0.003344208,0.00006402196,0.0001879774,0.0002751656,0.0001377948,0.7528805,0.001236845,0.2371735,0.004111275,0.0000771566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0140247,0.000154287,0.9845707,0.0002338258,0.00006083467,0.0002065602,0.0002866982,0.0002054802,0.0002569514],"genre_scores_gemma":[0.4970408,0.0007290827,0.4904465,0.0003652575,0.0002035037,0.003992075,0.001513778,0.0002468765,0.005462104],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04365297,"threshold_uncertainty_score":0.2308617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2797240781651692,"score_gpt":0.4778947637698633,"score_spread":0.1981706856046941,"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."}}