{"id":"W2954690927","doi":"10.5539/ijsp.v7n6p81","title":"Evaluation of Performance of Adaptive Designs Based on Treatment Effect Intervals","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health","keywords":"Sample size determination; Flexibility (engineering); Early stopping; Adaptive design; Interim; Selection (genetic algorithm); Computer science; Interim analysis; Treatment effect; Statistics; Sample (material); Mathematics; Clinical trial; Machine learning; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1193847,0.001886202,0.001812467,0.002564383,0.0006543217,0.001663796,0.002342104,0.002317647,0.003101194],"category_scores_gemma":[0.3458864,0.0007464901,0.001834876,0.00160299,0.001833364,0.002070795,0.002043886,0.002160162,0.0003703426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00153929,"about_ca_system_score_gemma":0.002441829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009376871,"about_ca_topic_score_gemma":0.0004287719,"domain_scores_codex":[0.9024503,0.07994776,0.004761778,0.003888007,0.008054585,0.0008976305],"domain_scores_gemma":[0.4429681,0.5042539,0.01831476,0.01830722,0.01459576,0.001560253],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02006974,0.001184586,0.03104722,0.002704034,0.002317949,0.0002136793,0.001142315,0.4572498,0.005217356,0.04561144,0.001736662,0.4315053],"study_design_scores_gemma":[0.00379321,0.01879215,0.01503477,0.000868206,0.001200842,0.0004145435,0.000302327,0.8971241,0.009765986,0.04760492,0.004807796,0.0002911331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1954736,0.003793543,0.7919933,0.0004059526,0.0002776839,0.003192757,0.0003517897,0.0006915273,0.003819699],"genre_scores_gemma":[0.5248744,0.000851139,0.4697914,0.0001714601,0.00009035054,0.003119522,0.0004146532,0.000126947,0.0005601266],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8806154,"threshold_uncertainty_score":0.631374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5405882645561305,"score_gpt":0.5548984099407206,"score_spread":0.01431014538459008,"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."}}