{"id":"W30694091","doi":"10.1007/978-1-4939-2428-8_6","title":"Longitudinal Studies 3: Data Modeling Using Standard Regression Models and Extensions","year":2015,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; Foothills Medical Centre; University of Calgary","funders":"","keywords":"Longitudinal data; Outcome (game theory); Generalized linear model; Linear regression; Regression analysis; Statistics; Linear model; Generalized linear mixed model; Econometrics; Mixed model; Computer science; Mathematics; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06515463,0.001814897,0.002383119,0.002175157,0.000804402,0.003385445,0.004263185,0.002976398,0.009293715],"category_scores_gemma":[0.1302141,0.001692091,0.005699516,0.003320676,0.002020001,0.005329498,0.003159479,0.004790681,0.00163672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120526,"about_ca_system_score_gemma":0.003496773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006673276,"about_ca_topic_score_gemma":0.006564321,"domain_scores_codex":[0.9744892,0.02026282,0.0009576834,0.002809562,0.001159015,0.00032163],"domain_scores_gemma":[0.887658,0.09088485,0.004986342,0.01235762,0.003291245,0.0008219525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007168463,0.0003643478,0.01940579,0.001861016,0.005071955,0.0005781157,0.001213475,0.08384312,0.0007346164,0.5869007,0.02406358,0.2752465],"study_design_scores_gemma":[0.0002024754,0.0002004844,0.00457927,0.0004747893,0.00108467,0.0003229774,0.0001441543,0.2171781,0.0003020305,0.7593954,0.016026,0.0000895423],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008292967,0.004523488,0.9818678,0.002555007,0.0002889091,0.0001443647,0.0008937189,0.0004962388,0.0009375003],"genre_scores_gemma":[0.1660456,0.01098088,0.8003613,0.002113858,0.001793211,0.002247116,0.00264225,0.0009471687,0.01286866],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06515463,"threshold_uncertainty_score":0.3445747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5966843994995419,"score_gpt":0.6003483513581748,"score_spread":0.003663951858632886,"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."}}