{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3364978,0.001761155,0.002026351,0.00255647,0.0021505,0.004040197,0.0026443,0.004070569,0.003515851],"category_scores_gemma":[0.5751511,0.001399752,0.004121699,0.003095515,0.006864207,0.004978411,0.003838127,0.006572519,0.000830612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003546569,"about_ca_system_score_gemma":0.00306644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003775193,"about_ca_topic_score_gemma":0.003356571,"domain_scores_codex":[0.7160695,0.2285633,0.01563342,0.01866162,0.01742535,0.003646805],"domain_scores_gemma":[0.153453,0.7829527,0.01809906,0.03575633,0.008712156,0.001026673],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01107534,0.0009648821,0.2811091,0.003208745,0.005868635,0.002602655,0.006677806,0.1852863,0.01150027,0.142403,0.014352,0.3349513],"study_design_scores_gemma":[0.001129444,0.002470775,0.07747065,0.004128051,0.003715812,0.003823387,0.001607672,0.565308,0.03665248,0.2874131,0.01510139,0.00117925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1650859,0.004557969,0.8150555,0.004098012,0.0008590473,0.00179756,0.0008753224,0.001692631,0.005978076],"genre_scores_gemma":[0.6756964,0.00112219,0.3151533,0.002382908,0.0001943049,0.002220128,0.0008160451,0.0009404283,0.001474307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6635022,"threshold_uncertainty_score":0.8182163,"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."}}