{"id":"W2890626091","doi":"10.1002/sim.7963","title":"Modeling semicontinuous longitudinal data with order constraints","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Inference; Computer science; Longitudinal data; Statistical inference; Econometrics; Statistical hypothesis testing; Joint (building); Machine learning; Statistics; Data mining; Artificial intelligence; Mathematics","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":[0.02852488,0.001251093,0.002791301,0.001715618,0.000752939,0.002840912,0.003640159,0.002449189,0.003247482],"category_scores_gemma":[0.08301015,0.001468829,0.002262107,0.002752103,0.003270574,0.003989852,0.002902214,0.004398377,0.0004619981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767697,"about_ca_system_score_gemma":0.002906624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009968839,"about_ca_topic_score_gemma":0.008799111,"domain_scores_codex":[0.9882079,0.007530226,0.0006692006,0.001828086,0.001199018,0.0005655503],"domain_scores_gemma":[0.8763337,0.1075143,0.007343202,0.005992546,0.002062228,0.0007540903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003222435,0.0001264457,0.01519458,0.0003560311,0.0003271977,0.001277749,0.0009455065,0.4941709,0.0009452243,0.4410191,0.001995667,0.04331934],"study_design_scores_gemma":[0.00004845035,0.00006449849,0.001864162,0.00006709052,0.00004871718,0.000167497,0.00005451842,0.6625278,0.0002012724,0.3337948,0.001127219,0.00003400884],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03191099,0.0006583119,0.9650033,0.0007827174,0.00004219608,0.00007458296,0.0004647059,0.0001729036,0.000890161],"genre_scores_gemma":[0.6245041,0.001994464,0.3652585,0.0008880154,0.0002502737,0.0008776425,0.001772859,0.0001959272,0.004258201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02852488,"threshold_uncertainty_score":0.1508558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1291474272563127,"score_gpt":0.4314943920152804,"score_spread":0.3023469647589677,"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."}}