{"id":"W2163628937","doi":"10.1093/biostatistics/kxn044","title":"Bias in 2-part mixed models for longitudinal semicontinuous data","year":2009,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Random effects model; Mixed model; Censoring (clinical trials); Statistics; Missing data; Econometrics; Monte Carlo method; Independence (probability theory); Mathematics; Variance (accounting); Computer science; Medicine; Meta-analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1397962,0.001633388,0.003230341,0.00271039,0.001243888,0.003560556,0.004547145,0.003828484,0.00270229],"category_scores_gemma":[0.2904781,0.001709961,0.003483499,0.003091359,0.005233283,0.004934683,0.003583633,0.00410327,0.0006338868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00257027,"about_ca_system_score_gemma":0.002087625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004003924,"about_ca_topic_score_gemma":0.004165641,"domain_scores_codex":[0.9422336,0.04747659,0.001997618,0.004270751,0.003223785,0.0007975874],"domain_scores_gemma":[0.6679393,0.3018777,0.01161289,0.01416226,0.003824858,0.0005830899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000865216,0.0001301166,0.02864485,0.0009769605,0.001176783,0.001379118,0.002389824,0.2507771,0.001663789,0.6084085,0.002228294,0.1013595],"study_design_scores_gemma":[0.00008527528,0.0001879377,0.003498452,0.0002062412,0.0001668883,0.0004449688,0.000201575,0.6232392,0.00093668,0.3686312,0.002300711,0.0001008414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01064143,0.0004886554,0.9876723,0.0004452311,0.00006114782,0.0001115677,0.0001146731,0.0001903946,0.0002745992],"genre_scores_gemma":[0.3402636,0.001191515,0.6517494,0.001196333,0.0002491859,0.001444111,0.0007666952,0.0002274779,0.002911599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1397962,"threshold_uncertainty_score":0.7393216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4566833157976524,"score_gpt":0.4407942544428088,"score_spread":0.01588906135484358,"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."}}