{"id":"W4309915763","doi":"10.1002/sim.9606","title":"Adjusting for treatment selection in phase II/III clinical trials with time to event data","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Novartis Pharma; Medical Research Council Canada","keywords":"Estimator; Statistics; Selection (genetic algorithm); Stage (stratigraphy); U-statistic; Selection bias; Mean squared error; Variance (accounting); Clinical trial; Efficiency; Mathematics; Computer science; Econometrics; Medicine; Internal medicine; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1835623,0.001167854,0.00216229,0.001882723,0.0007578102,0.002057811,0.002189159,0.002302566,0.002270646],"category_scores_gemma":[0.4775808,0.0008197512,0.003017829,0.002827143,0.001535871,0.002362091,0.001685045,0.003053832,0.0004069824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001526714,"about_ca_system_score_gemma":0.004442551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001442459,"about_ca_topic_score_gemma":0.001831248,"domain_scores_codex":[0.7749473,0.2021196,0.007909524,0.005181312,0.008671832,0.001170539],"domain_scores_gemma":[0.6412518,0.3047421,0.02214572,0.02122739,0.009565152,0.001067753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005230214,0.0005240644,0.06023437,0.003937995,0.007100206,0.0003565026,0.001066512,0.1318882,0.003632306,0.1045974,0.01036098,0.6710712],"study_design_scores_gemma":[0.004725504,0.00499663,0.02827913,0.002534615,0.005464605,0.000626543,0.0002302586,0.6712813,0.01536471,0.2273693,0.03874906,0.0003784102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01026311,0.002023691,0.9837672,0.0008010339,0.0002538101,0.001541491,0.000183654,0.0004572793,0.0007087656],"genre_scores_gemma":[0.3063825,0.001225814,0.6839023,0.002105776,0.0002103008,0.004691396,0.0004924522,0.0002131801,0.0007762996],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1835623,"threshold_uncertainty_score":0.9707816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7994143033413676,"score_gpt":0.7010730436421652,"score_spread":0.09834125969920249,"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."}}