{"id":"W2061743155","doi":"10.1016/j.prevetmed.2009.10.004","title":"A simulation study to assess statistical methods for binary repeated measures data","year":2009,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Marginal model; Autocorrelation; Statistics; Random effects model; Generalized estimating equation; Binary data; Mathematics; Autoregressive model; Marginal likelihood; Marginal distribution; Markov chain Monte Carlo; Bayesian probability; Econometrics; Binary number; Random variable; Regression analysis; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.001160069,0.0001889055,0.0002523571,0.00005936219,0.00008700259,0.000009147658,0.0004003301,0.00005958719,0.00003643457],"category_scores_gemma":[0.001345504,0.0001597982,0.00002919958,0.0001103082,0.00005766105,0.000006312873,0.0002031596,0.00007038128,0.000002308324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001069303,"about_ca_system_score_gemma":0.00004020209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000660554,"about_ca_topic_score_gemma":0.000001485393,"domain_scores_codex":[0.9981488,0.0005245934,0.0003183854,0.0006302271,0.0001523546,0.0002256527],"domain_scores_gemma":[0.9986641,0.0002337502,0.00007607426,0.0007512419,0.0001400577,0.0001347406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004531102,0.002589872,0.001148633,0.00008348665,0.0004603382,0.00001681825,0.001522836,0.007789053,0.6056283,0.0006179519,0.006140349,0.3694713],"study_design_scores_gemma":[0.006808433,0.1412775,0.7460196,0.0002106628,0.000702901,0.00005034031,0.001722698,0.008703464,0.003881142,0.007466875,0.08202562,0.001130777],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2757711,0.0003231368,0.7222485,0.0001820031,0.0002021267,0.001043384,0.00005845034,0.00001733865,0.000153997],"genre_scores_gemma":[0.7613387,0.000004562045,0.2373954,0.0001999624,0.0002787829,0.00003811852,0.00046035,0.00001478363,0.0002693462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.744871,"threshold_uncertainty_score":0.6516383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1913841175224313,"score_gpt":0.4822661042632869,"score_spread":0.2908819867408556,"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."}}