{"id":"W2442085826","doi":"10.1007/978-1-59745-385-1_4","title":"Modeling Longitudinal Data, II: Standard Regression Models and Extensions","year":2008,"lang":"en","type":"review","venue":"Methods in molecular biology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Longitudinal data; Outcome (game theory); Generalized linear model; Linear regression; Regression analysis; Statistics; Linear model; Generalized linear mixed model; Regression; Mixed model; Econometrics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002480317,0.0005997672,0.002879089,0.0003293055,0.0001804911,0.00002307005,0.0007130783,0.000799404,0.00003166583],"category_scores_gemma":[0.005595054,0.0004240557,0.0001961745,0.0003325915,0.0002767733,0.00005880066,0.001500235,0.0009426263,0.000001516108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006801212,"about_ca_system_score_gemma":0.0002342467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002774448,"about_ca_topic_score_gemma":0.000004988647,"domain_scores_codex":[0.9927034,0.00423576,0.001123903,0.001247841,0.0001710932,0.0005180296],"domain_scores_gemma":[0.9950559,0.002904272,0.0002879888,0.001503761,0.0001000458,0.0001480143],"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":[0.00000810003,0.00003185877,4.973785e-7,0.00129454,0.00005569333,0.0001254672,0.00002371705,0.000003214765,0.00001600738,0.1508407,0.00004876418,0.8475514],"study_design_scores_gemma":[0.0001818154,0.0001173036,5.300844e-8,0.005236892,0.0003334039,0.0003430072,0.00000652161,0.0400079,0.000006121872,0.9080678,0.04517628,0.0005228686],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[4.978656e-7,0.4828835,0.516437,0.000009588547,0.0001217103,0.0002856176,0.0001425729,0.00002388554,0.00009564304],"genre_scores_gemma":[1.060939e-7,0.4951002,0.5047246,0.00001341522,0.00002657396,0.00003345157,0.00005488084,0.00003905061,0.000007747649],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8470286,"threshold_uncertainty_score":0.9998211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3665390658237885,"score_gpt":0.5666947209346396,"score_spread":0.2001556551108511,"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."}}