{"id":"W7117115668","doi":"10.3390/stats9010001","title":"A Proportional Hazards Mixture Cure Model for Subgroup Analysis: Inferential Method and an Application to Colon Cancer Data","year":2025,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Levamisole; Cure rate; Colorectal cancer; Proportional hazards model; Cancer; Mixture model; Maximum likelihood","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002420861,0.0001499693,0.0005170131,0.000128726,0.00009578479,0.00005407138,0.0003368286,0.0001515957,0.0000407472],"category_scores_gemma":[0.009016977,0.0001289291,0.00006644255,0.000463489,0.00004987945,0.00009616176,0.000212447,0.0001435714,7.95504e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004247276,"about_ca_system_score_gemma":0.0002250989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004604795,"about_ca_topic_score_gemma":0.0006292964,"domain_scores_codex":[0.9980416,0.0002809086,0.0005772476,0.0006415588,0.0002514833,0.0002071431],"domain_scores_gemma":[0.9941525,0.004539355,0.0001778579,0.0007444232,0.000253574,0.0001322285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009465462,0.0004961445,0.001051787,0.0006188251,0.001512919,0.00000104193,0.0004323133,0.002113787,0.001586136,0.7572876,0.02111911,0.2128339],"study_design_scores_gemma":[0.0003540408,0.00003657032,0.0003630393,0.00001742006,0.0009966388,8.966399e-8,0.00001042497,0.417608,0.0001299083,0.5798044,0.0005863996,0.00009308266],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003221234,0.0000316621,0.9901848,0.001152404,0.0001301793,0.00130872,0.003856336,0.00005172691,0.00006295944],"genre_scores_gemma":[0.03417682,0.00001406199,0.9641041,0.0003498952,0.0001591184,0.0006452684,0.0002063615,0.00001693054,0.0003274886],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4154942,"threshold_uncertainty_score":0.9993305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4864509021177346,"score_gpt":0.6537123222113409,"score_spread":0.1672614200936063,"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."}}