{"id":"W2140085139","doi":"10.1002/sim.3552","title":"Bayesian adjustment for covariate measurement errors: A flexible parametric approach","year":2009,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Covariate; Bayesian probability; Statistics; Parametric statistics; Computer science; Econometrics; Semiparametric model; Observational error; Mathematics","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":[0.03519347,0.001781537,0.003156064,0.002429042,0.001383404,0.002212874,0.006269305,0.00286793,0.003168434],"category_scores_gemma":[0.1173962,0.001576601,0.003588242,0.003740403,0.0025979,0.003884283,0.00466786,0.005317197,0.0007939182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389032,"about_ca_system_score_gemma":0.004101363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008193914,"about_ca_topic_score_gemma":0.006280535,"domain_scores_codex":[0.978308,0.01533307,0.0007529999,0.002513133,0.002453308,0.0006394629],"domain_scores_gemma":[0.9367611,0.05019559,0.003292812,0.006069789,0.003214805,0.0004658164],"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":[0.0004894225,0.0002002483,0.008535261,0.0006004284,0.00131316,0.0006590851,0.0009989714,0.4717046,0.001859091,0.1847422,0.004917161,0.3239804],"study_design_scores_gemma":[0.00009586139,0.000107342,0.00166501,0.0001105899,0.0002155582,0.0002169978,0.00007143379,0.8465583,0.0007231845,0.1458341,0.004313293,0.00008841176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001433193,0.0001726347,0.9978622,0.0001225057,0.00002253486,0.0000483983,0.00003645458,0.0001298678,0.000172255],"genre_scores_gemma":[0.1937344,0.001015952,0.8001835,0.0005204269,0.0003245085,0.0008575795,0.0005960272,0.0003105502,0.002457056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03519347,"threshold_uncertainty_score":0.1861231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1722678694824186,"score_gpt":0.4195246405955843,"score_spread":0.2472567711131657,"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."}}