{"id":"W1988018581","doi":"10.1002/sim.1748","title":"Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Covariate; Censoring (clinical trials); Inference; Statistics; Computer science; Statistical inference; Gibbs sampling; Data set; Monte Carlo method; Observational error; Econometrics; Mathematics; Artificial intelligence; Bayesian probability","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":[0.03825029,0.001623077,0.003187225,0.004036502,0.001234092,0.002460269,0.003058859,0.002778986,0.001550303],"category_scores_gemma":[0.1949885,0.001977551,0.002154195,0.005082064,0.003161,0.003891851,0.004234998,0.003821776,0.0003080554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316381,"about_ca_system_score_gemma":0.002160543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005918398,"about_ca_topic_score_gemma":0.005294298,"domain_scores_codex":[0.9800562,0.01530422,0.0007089333,0.001954995,0.001751297,0.0002243011],"domain_scores_gemma":[0.8307816,0.15596,0.005906678,0.004357066,0.002362302,0.0006323728],"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":[0.000619256,0.0002081768,0.01953295,0.001047267,0.001929953,0.0007049298,0.001593678,0.3698983,0.002454536,0.2857654,0.002619003,0.3136266],"study_design_scores_gemma":[0.0001268721,0.0001204958,0.003142476,0.0001396639,0.0002505513,0.0002476427,0.00009618136,0.673583,0.0009441442,0.3187867,0.002492099,0.00007018891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00571062,0.0007430741,0.9929739,0.0002211578,0.00002619381,0.00003738271,0.00004528925,0.0001121777,0.0001302086],"genre_scores_gemma":[0.205718,0.002032384,0.7894478,0.0002772302,0.00019491,0.0005720062,0.0003960949,0.0001232118,0.00123843],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03825029,"threshold_uncertainty_score":0.2022893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05464080793646976,"score_gpt":0.3630267823652393,"score_spread":0.3083859744287695,"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."}}