{"id":"W4386010298","doi":"10.1002/sim.9879","title":"A threshold longitudinal Tobit quantile regression model for identification of treatment‐sensitive subgroups based on interval‐bounded longitudinal measurements and a continuous covariate","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Science Foundation of Anhui Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Covariate; Statistics; Estimator; Mathematics; Tobit model; Econometrics; Quantile; Confidence interval; Random effects model; Identification (biology); Regression analysis; Quantile regression; Computer science; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001628647,0.0002417441,0.0006885698,0.0002578993,0.00008622719,0.00001972859,0.0001008333,0.00008196211,0.0000207222],"category_scores_gemma":[0.00529285,0.0001797466,0.00003810635,0.0002694815,0.00026016,0.00003560335,0.0000303061,0.0001207201,0.000002571737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001396524,"about_ca_system_score_gemma":0.0000740809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001093547,"about_ca_topic_score_gemma":0.0001389871,"domain_scores_codex":[0.9979447,0.000131719,0.0007975804,0.0004188,0.000420099,0.0002870637],"domain_scores_gemma":[0.9958749,0.003014251,0.0003683443,0.0003030415,0.0003512063,0.0000883008],"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":[0.003670546,0.001138508,0.02145187,0.001915334,0.0003322262,0.000121274,0.006456622,0.001520241,0.01659675,0.9219059,0.007331265,0.01755945],"study_design_scores_gemma":[0.002371755,0.001181529,0.009959092,0.0006688211,0.0001522313,0.000002261442,0.0002191098,0.7060572,0.0008222007,0.2784278,0.000002614461,0.0001354408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07343205,0.00001581597,0.9246027,0.0001373089,0.0001903511,0.0007259756,0.0007291106,0.00003682483,0.0001298275],"genre_scores_gemma":[0.8488518,0.00002832952,0.1506684,0.00003011157,0.00003675914,0.00009434498,0.0001160298,0.00002653867,0.0001476638],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7754198,"threshold_uncertainty_score":0.7329854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2717777404100385,"score_gpt":0.443233959218317,"score_spread":0.1714562188082784,"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."}}