{"id":"W2799837142","doi":"10.1007/s10260-018-0428-0","title":"Modeling right-censored medical cost data in regression and the effects of covariates","year":2018,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Public Health Ontario; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Covariate; Censoring (clinical trials); Estimator; Econometrics; Statistics; Regression analysis; Survival analysis; Survival function; Computer science; Generalized linear model; Regression; Linear regression; Mathematics","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":[0.03305771,0.001382033,0.00281234,0.001717791,0.0005116977,0.00295919,0.004180377,0.003680934,0.00421719],"category_scores_gemma":[0.1346913,0.001600922,0.002683768,0.003855151,0.001928402,0.003750948,0.001690679,0.004256701,0.0008807833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001522219,"about_ca_system_score_gemma":0.002454428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02148475,"about_ca_topic_score_gemma":0.01509202,"domain_scores_codex":[0.9778484,0.01745947,0.0007771692,0.002163679,0.0008732909,0.0008779752],"domain_scores_gemma":[0.806079,0.1746726,0.008831803,0.007460687,0.002120801,0.0008350505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001271762,0.000552185,0.07879087,0.0007000646,0.001956275,0.0007565357,0.0006554009,0.6996479,0.0007182247,0.1473258,0.005514772,0.06211019],"study_design_scores_gemma":[0.0001295868,0.0001474023,0.01043861,0.0001615714,0.0003453915,0.0001300193,0.00008994288,0.9038211,0.0004033979,0.08172188,0.002531129,0.0000799289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1767483,0.004689572,0.8061554,0.005806934,0.000480926,0.0001714078,0.003547271,0.0006864393,0.001713749],"genre_scores_gemma":[0.8920752,0.00346609,0.08776964,0.001090978,0.0008236541,0.0004526923,0.003842835,0.0003183518,0.01016049],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03305771,"threshold_uncertainty_score":0.1748279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08309943434718564,"score_gpt":0.4317956276211494,"score_spread":0.3486961932739638,"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."}}