{"id":"W4403691889","doi":"10.1177/09622802241282091","title":"Applying survey weights to ordinal regression models for improved inference in outcome-dependent samples with ordinal outcomes","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Institute for Work & Health; Western University; Public Health Ontario; University of Toronto","funders":"Agencia Estatal de Investigación; Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación; University of Toronto; Instituto de Salud Carlos III; Alliance de recherche numérique du Canada; Generalitat de Catalunya; Innovation, Science and Economic Development Canada","keywords":"Statistics; Ordinal regression; Mathematics; Econometrics; Logistic regression; Ordinal data; Ordered logit; Logit; Outcome (game theory); Regression analysis; Sampling bias; Sample size determination","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":[],"consensus_categories":[],"category_scores_codex":[0.1782217,0.003201604,0.004051018,0.006203406,0.001468004,0.004241765,0.005495294,0.003089693,0.005626606],"category_scores_gemma":[0.4321811,0.002358113,0.005492483,0.009030703,0.002748983,0.00585471,0.005558943,0.007110803,0.0009743032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002278335,"about_ca_system_score_gemma":0.003394921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01112965,"about_ca_topic_score_gemma":0.01052763,"domain_scores_codex":[0.803495,0.1776716,0.005079777,0.007356644,0.005362288,0.001034648],"domain_scores_gemma":[0.6390325,0.3132305,0.0128394,0.0260591,0.008017847,0.0008206783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009830003,0.0004415115,0.03947698,0.002483847,0.005483998,0.0007878227,0.002388508,0.4361495,0.001618145,0.187322,0.005347419,0.3175172],"study_design_scores_gemma":[0.0003312144,0.0004129776,0.004118953,0.0004734691,0.0006052552,0.0001587551,0.0002406045,0.780439,0.0009827653,0.2049907,0.007103754,0.0001425513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006530614,0.0004898166,0.9911412,0.000272037,0.0001400491,0.0004313486,0.0002347155,0.0003865474,0.0003735871],"genre_scores_gemma":[0.1473106,0.0008898875,0.8463051,0.0005104398,0.0002182157,0.002824099,0.0007913143,0.0002716811,0.0008785567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1782217,"threshold_uncertainty_score":0.9425377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4117070033392049,"score_gpt":0.6242060080290859,"score_spread":0.212499004689881,"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."}}