{"id":"W2035595990","doi":"10.1007/s10260-013-0242-7","title":"Jointly modeling time-to-event and longitudinal data: a Bayesian approach","year":2013,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of Allergy and Infectious Diseases; National Institute of Mental Health","keywords":"Covariate; Event (particle physics); Bayesian probability; Econometrics; Statistics; Nonparametric statistics; Posterior probability; Computer science; Posterior predictive distribution; Parametric statistics; Bayesian inference; Mathematics; Bayesian linear regression","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":[0.02947088,0.002576485,0.006619725,0.006426802,0.001386947,0.00517395,0.008345134,0.006382642,0.004382457],"category_scores_gemma":[0.08166161,0.003810635,0.005078709,0.00687131,0.003310204,0.008387866,0.00473481,0.007536181,0.00142897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002320116,"about_ca_system_score_gemma":0.004173884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01646999,"about_ca_topic_score_gemma":0.01607099,"domain_scores_codex":[0.9858773,0.008190046,0.0007271378,0.003129908,0.001428555,0.0006471199],"domain_scores_gemma":[0.9262125,0.06466925,0.003386014,0.003076522,0.001817181,0.000838447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006787036,0.0005773832,0.01528686,0.0007169722,0.00263299,0.0006692814,0.0009144878,0.4828879,0.001306664,0.3426478,0.005674327,0.1460066],"study_design_scores_gemma":[0.0001022773,0.00009017522,0.002162891,0.0001394486,0.000445184,0.0003204831,0.00009191589,0.7135068,0.0002588816,0.2798245,0.002923134,0.0001344117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003823875,0.001162029,0.9934789,0.0006307875,0.00006772287,0.00005270643,0.0002949405,0.0001402525,0.0003488363],"genre_scores_gemma":[0.2989745,0.008417888,0.6763051,0.001192353,0.001366852,0.001563023,0.003559843,0.0003507463,0.008269589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02947088,"threshold_uncertainty_score":0.1558588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05491215545635405,"score_gpt":0.376205657299214,"score_spread":0.3212935018428599,"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."}}