{"id":"W2186127712","doi":"10.1002/cjs.11273","title":"A semivarying joint model for longitudinal binary and continuous outcomes","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institute on Drug Abuse; National Institutes of Health; National Science Foundation","keywords":"Covariate; Estimator; Binary number; Statistics; Multivariate statistics; Asymptotic distribution; Binary data; Latent variable; Mathematics; Marginal model; Computer science; Latent variable model; Multivariate normal distribution; Marginal distribution; Constant (computer programming); Econometrics; Regression analysis; Random variable","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006741296,0.0001376892,0.0004419605,0.0001509854,0.00009092572,0.00007599116,0.0001106507,0.00005925931,0.00002044453],"category_scores_gemma":[0.005484337,0.0001139186,0.00004543481,0.00005585528,0.0001255209,0.000077253,0.00001280339,0.0001690611,0.000001058054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000100287,"about_ca_system_score_gemma":0.00103465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002358184,"about_ca_topic_score_gemma":0.001146954,"domain_scores_codex":[0.9988981,0.00004657796,0.0004915486,0.0001107536,0.0001621029,0.0002908634],"domain_scores_gemma":[0.9972229,0.0009792602,0.0002645926,0.0001085401,0.0005239011,0.0009008111],"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":[0.00003255553,0.00002411427,0.007728579,0.0001696077,0.00008264253,0.0003637806,0.001561295,0.00003964958,0.0000180744,0.9221979,0.04742045,0.02036137],"study_design_scores_gemma":[0.0006663144,0.0002525985,0.001400194,0.0001094663,0.0001119131,0.0001683844,0.0002802727,0.04680533,0.000008046192,0.9497082,0.0003358525,0.0001534163],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003900211,0.0001931311,0.9940684,0.0005499487,0.0002241835,0.0001288435,0.0007817665,0.000004678143,0.0001488746],"genre_scores_gemma":[0.1215161,0.00001202592,0.8781664,0.0000877135,0.00005716351,0.000002939759,0.000002166491,0.00001974512,0.0001356996],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1176159,"threshold_uncertainty_score":0.656566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1988227043769351,"score_gpt":0.3623722910237851,"score_spread":0.16354958664685,"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."}}