{"id":"W4328048262","doi":"10.1093/bioinformatics/btad139","title":"The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Covariate; Biobank; Sample size determination; Computer science; Trait; Statistics; Sample (material); Logistic regression; Binary number; Data mining; Replication (statistics); Econometrics; Mathematics; Biology; Bioinformatics","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":[0.05500855,0.001376162,0.001766757,0.001250574,0.0008171511,0.002135136,0.004276687,0.003296421,0.006625977],"category_scores_gemma":[0.21977,0.001060001,0.00182394,0.002259082,0.003417924,0.003324646,0.003948344,0.003995097,0.0013045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208709,"about_ca_system_score_gemma":0.004131746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002915265,"about_ca_topic_score_gemma":0.002691979,"domain_scores_codex":[0.9568338,0.03509106,0.001245225,0.003635588,0.002687453,0.0005068973],"domain_scores_gemma":[0.8271141,0.1482802,0.005083746,0.01518425,0.003237298,0.001100329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002063396,0.0002560298,0.02646076,0.002175749,0.001077271,0.0007217615,0.001895807,0.1448257,0.006609365,0.1947542,0.01843056,0.6007294],"study_design_scores_gemma":[0.0009975444,0.0007690391,0.008363786,0.0008496849,0.0004891385,0.0007516213,0.0001399865,0.5715709,0.006089504,0.3872927,0.02250624,0.0001797081],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002404971,0.0003442591,0.9953735,0.0005725559,0.00007631596,0.0002964678,0.0001488245,0.000290262,0.0004929112],"genre_scores_gemma":[0.09412636,0.0004096275,0.901154,0.000575204,0.0002500435,0.00202078,0.0002788267,0.0002935807,0.000891513],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05500855,"threshold_uncertainty_score":0.2909164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192996970306229,"score_gpt":0.2859291187542457,"score_spread":0.2666294217236228,"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."}}