{"id":"W3209165885","doi":"10.1016/j.ecosta.2021.10.015","title":"A Markov decision process for response adaptive designs","year":2021,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov decision process; Mathematical optimization; Markov chain; Markov process; Operator (biology); Process (computing); Partially observable Markov decision process; Mathematics; Markov model; Value (mathematics); Computer science; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03985193,0.002264892,0.004296863,0.002160518,0.001043373,0.002642222,0.004236429,0.004403891,0.01069295],"category_scores_gemma":[0.09545239,0.002542887,0.00306553,0.002562128,0.003825926,0.004384609,0.003897168,0.006641843,0.001919447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002401478,"about_ca_system_score_gemma":0.005055482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003962054,"about_ca_topic_score_gemma":0.002783064,"domain_scores_codex":[0.9719491,0.02107706,0.0009989187,0.002677822,0.002496074,0.0008009973],"domain_scores_gemma":[0.8806616,0.1086067,0.003045317,0.003544295,0.003168453,0.0009735209],"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":[0.0003080388,0.00009386944,0.0006932686,0.000251216,0.0001889815,0.0001613967,0.0001774404,0.1821186,0.0006131444,0.7771164,0.002483286,0.03579446],"study_design_scores_gemma":[0.0001816434,0.0001345927,0.0002064307,0.00007681728,0.00007447893,0.00008906568,0.00001415378,0.5938166,0.0002186885,0.4030471,0.002095395,0.00004494296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001025342,0.0001921116,0.9978453,0.000277679,0.00004116292,0.00007344342,0.00006680165,0.000073216,0.0004049575],"genre_scores_gemma":[0.1703824,0.001740543,0.8155877,0.0009554013,0.000493076,0.002420719,0.0007078204,0.0002087325,0.007503566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03985193,"threshold_uncertainty_score":0.2107596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7140437471766378,"score_gpt":0.566349235593182,"score_spread":0.1476945115834558,"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."}}