{"id":"W2164652223","doi":"10.1002/sim.6387","title":"Effects of categorization method, regression type, and variable distribution on the inflation of Type‐I error rate when categorizing a confounding variable","year":2014,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Q & T Research; Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Statistics; Confounding; Econometrics; Proxy (statistics); Type I and type II errors; Logistic regression; Categorization; Latent variable; Variable (mathematics); Regression analysis; Regression; Mathematics; Inflation (cosmology); Linear regression; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002646921,0.0001361423,0.0004097015,0.00006720574,0.00006896717,0.000005770266,0.00007562056,0.00007930933,0.00003119705],"category_scores_gemma":[0.02960396,0.00008726688,0.000006536538,0.0003362677,0.0001253787,0.00004928417,0.00003206069,0.0001803423,4.637243e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004574764,"about_ca_system_score_gemma":0.00004279589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001179693,"about_ca_topic_score_gemma":0.000004710186,"domain_scores_codex":[0.9983481,0.0005944135,0.0004834327,0.0001854044,0.000230544,0.0001581005],"domain_scores_gemma":[0.9872348,0.01181678,0.0003608736,0.0001857351,0.0003563163,0.00004544058],"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":[0.00009853811,0.00002878208,0.00005311815,0.0005811297,0.00001401346,9.357449e-7,0.0004908599,0.0002744745,0.01956624,0.9751158,0.0004471169,0.003329024],"study_design_scores_gemma":[0.000632805,0.000334883,0.0002197977,0.0005873305,0.00009341486,0.000001017343,0.00008188216,0.09988285,0.003200732,0.8945891,0.0002957147,0.00008048373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002238638,0.00004112929,0.9963813,0.00007945143,0.0002746504,0.000346219,0.00003757771,0.00001104365,0.0005899977],"genre_scores_gemma":[0.1790196,0.00005298025,0.8205523,0.00005101658,0.00006394816,0.00001202622,0.0001065382,0.00001986493,0.0001216485],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.176781,"threshold_uncertainty_score":0.9785701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05918741910574839,"score_gpt":0.4150197663453947,"score_spread":0.3558323472396464,"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."}}