{"id":"W2004939947","doi":"10.1016/j.csda.2007.04.018","title":"Mixture cure models for multivariate survival data","year":2007,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Censoring (clinical trials); Statistics; Covariate; Multivariate statistics; Bivariate analysis; Jackknife resampling; Mathematics; Survival analysis; Marginal model; Survival function; Marginal distribution; Random effects model; Multivariate analysis; Econometrics; Correlation; Regression analysis; Medicine; Random variable; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002978333,0.0002672001,0.0004850928,0.0003328808,0.0002950609,0.0003282097,0.004085424,0.0001097026,0.00001924124],"category_scores_gemma":[0.0003132739,0.0002550517,0.00009374362,0.001319437,0.00006515074,0.001154038,0.001808616,0.0001903169,0.000009110466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003916321,"about_ca_system_score_gemma":0.000182942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002407244,"about_ca_topic_score_gemma":0.0004154935,"domain_scores_codex":[0.9967318,0.0001609195,0.0006215435,0.001355924,0.0006739339,0.0004558934],"domain_scores_gemma":[0.9940701,0.001922299,0.0002821769,0.003008758,0.0004937755,0.000222948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002208064,0.0001106846,0.0001177793,0.00002524557,0.001126122,0.00002184956,0.0001385975,0.06533168,0.000006649047,0.8140328,0.02084807,0.09821843],"study_design_scores_gemma":[0.0002557804,0.00001201344,0.001205894,0.000003590222,0.000494224,0.000002021005,0.00000265947,0.6687246,0.000001434131,0.325817,0.003271025,0.000209679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000007677857,0.0001705607,0.9775237,0.0004211731,0.0002967923,0.0002247134,0.02111774,0.00008623282,0.0001514263],"genre_scores_gemma":[0.01156915,0.00002214224,0.9503093,0.0003055372,0.0001829776,0.000004522794,0.03747723,0.00001951466,0.0001096589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.603393,"threshold_uncertainty_score":0.9999902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1092255275723662,"score_gpt":0.3880269756108853,"score_spread":0.278801448038519,"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."}}