{"id":"W2016919144","doi":"10.1002/sim.2711","title":"Bayesian sensitivity analysis for unmeasured confounding in observational studies","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Confounding; Statistics; Markov chain Monte Carlo; Bayesian probability; Observational study; Econometrics; Logistic regression; Prior probability; Sample size determination; Sensitivity (control systems); Mathematics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00294536,0.0001768332,0.0008009552,0.0003628785,0.00006371525,0.00001171296,0.00007116511,0.00006606093,0.00006737946],"category_scores_gemma":[0.01805763,0.0001502257,0.00003818837,0.0008277185,0.0002536201,0.00003833509,0.00002152377,0.0001678869,8.42037e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001611168,"about_ca_system_score_gemma":0.00005240186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003665136,"about_ca_topic_score_gemma":0.005885283,"domain_scores_codex":[0.9979748,0.000286481,0.0007590422,0.0002980111,0.0003753425,0.0003063596],"domain_scores_gemma":[0.98395,0.01535692,0.0001767653,0.0001837016,0.0002851692,0.00004741342],"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":[0.00003397561,0.00006637172,0.05800291,0.0001833353,0.0001140439,0.00005978397,0.0003161396,0.0000754658,0.0001250078,0.9362516,0.002197913,0.002573407],"study_design_scores_gemma":[0.0008069526,0.00005678313,0.1207594,0.0001381655,0.0002324105,0.000001663247,0.0003050424,0.03279477,0.00001823679,0.8446822,0.00006386801,0.0001405755],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00247425,0.00008674066,0.9955192,0.0005225808,0.0001829299,0.0003179997,0.000318397,0.00002004719,0.0005578473],"genre_scores_gemma":[0.3052233,0.00001311953,0.6943384,0.00008847916,0.0001301022,0.0000382837,0.000082087,0.00001172839,0.00007450845],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.302749,"threshold_uncertainty_score":0.9902137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2203225194206481,"score_gpt":0.4762013753441512,"score_spread":0.2558788559235031,"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."}}