{"id":"W1980915258","doi":"10.6000/1929-6029.2014.03.02.4","title":"A Bayesian Shared Parameter Model for Analysing Longitudinal Skewed Responses with Nonignorable Dropout","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Skew; Skewness; Markov chain Monte Carlo; Deviance information criterion; Missing data; Deviance (statistics); Bayesian probability; Computer science; Random effects model; Statistics; Data set; Dropout (neural networks); Mixed model; Econometrics; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03517948,0.001650127,0.00386046,0.002352344,0.001047753,0.002199108,0.005737891,0.003845774,0.005441587],"category_scores_gemma":[0.06307007,0.001417254,0.00299036,0.003012786,0.002709943,0.003844921,0.003006249,0.004036676,0.001105016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001796676,"about_ca_system_score_gemma":0.003867092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007513809,"about_ca_topic_score_gemma":0.005888168,"domain_scores_codex":[0.9838998,0.01128445,0.0005967405,0.002203366,0.001414125,0.0006014722],"domain_scores_gemma":[0.9664257,0.02630401,0.002120072,0.002557856,0.002067131,0.0005252776],"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":[0.001398187,0.0004485726,0.01659652,0.0008876448,0.001228646,0.001061019,0.001764304,0.4024017,0.003389053,0.3629512,0.005629301,0.2022438],"study_design_scores_gemma":[0.0001779712,0.0003331264,0.003269154,0.0001646699,0.0003135178,0.000289729,0.0001476429,0.8308362,0.0006349804,0.1597789,0.003943554,0.0001104285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01016546,0.0003662144,0.9876336,0.0003981586,0.000051835,0.000203508,0.0004046162,0.0001823013,0.0005943663],"genre_scores_gemma":[0.3771501,0.001823416,0.6035132,0.0008978972,0.0003475218,0.003755244,0.002640575,0.000243319,0.009628655],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03517948,"threshold_uncertainty_score":0.1860491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1877330039997778,"score_gpt":0.521479591404308,"score_spread":0.3337465874045302,"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."}}