{"id":"W2170987805","doi":"10.1002/sim.5378","title":"Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials","year":2012,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Science Foundation","keywords":"Breast cancer; Feature selection; Proportional hazards model; Computer science; Logistic regression; Model selection; Semiparametric regression; Accelerated failure time model; Clinical trial; Cure rate; Variable (mathematics); Statistics; Regression analysis; Econometrics; Cancer; Medicine; Mathematics; Artificial intelligence; Machine learning; Internal medicine","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":[0.04374678,0.00117955,0.002327197,0.002061708,0.0006360621,0.001711813,0.002267466,0.001824147,0.001820997],"category_scores_gemma":[0.1004008,0.001198979,0.001680972,0.002109257,0.002661214,0.002200578,0.002469707,0.003072865,0.0003072046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205669,"about_ca_system_score_gemma":0.001739868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001800599,"about_ca_topic_score_gemma":0.001554654,"domain_scores_codex":[0.9666588,0.03108062,0.0004756282,0.0006951593,0.0008028247,0.00028701],"domain_scores_gemma":[0.869341,0.1213486,0.004182687,0.002385403,0.002043878,0.0006985606],"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":[0.0006135621,0.0001083702,0.004036967,0.0004265022,0.000348616,0.0003711186,0.000353844,0.8173328,0.0006082894,0.1217195,0.002188275,0.05189213],"study_design_scores_gemma":[0.00007955181,0.00007013008,0.0002825035,0.00003241219,0.00003013506,0.00005544446,0.00001437155,0.9642273,0.0001603641,0.03455881,0.0004701529,0.0000188845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01011584,0.0007287294,0.9877963,0.0006406882,0.00003320696,0.0001439388,0.00004755861,0.0001607617,0.0003329513],"genre_scores_gemma":[0.4909736,0.001505042,0.5014634,0.000558999,0.000255265,0.002186834,0.0004036538,0.000265,0.002388316],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04374678,"threshold_uncertainty_score":0.2313579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1729665363887807,"score_gpt":0.5074455559875256,"score_spread":0.3344790195987449,"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."}}