{"id":"W1969551119","doi":"10.1080/03610920601125920","title":"Statistical Inference of Adaptive Designs with Binary Responses","year":2007,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Health Sciences Centre Foundation","keywords":"Estimator; Test statistic; Contingency table; Statistical hypothesis testing; Statistical inference; Statistics; Mathematics; Consistency (knowledge bases); Goodness of fit; Statistic; Logarithm; Sufficient statistic; Adaptive design; Inference; Restricted randomization; Computer science; Clinical trial; Randomization; Artificial intelligence; Medicine","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":["metaresearch"],"category_scores_codex":[0.03785541,0.0002267713,0.0007242072,0.0002037705,0.0001186094,0.00002044582,0.0004151965,0.0001632868,0.0001748756],"category_scores_gemma":[0.1604677,0.0001874954,0.00002862461,0.0003547713,0.00156796,0.00007936497,0.0002273792,0.0005124465,0.000002151775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004654925,"about_ca_system_score_gemma":0.0001289091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002020065,"about_ca_topic_score_gemma":0.00002683285,"domain_scores_codex":[0.9812732,0.01640494,0.001342966,0.0003528525,0.0002801287,0.0003459434],"domain_scores_gemma":[0.6629996,0.3356266,0.0003595949,0.0006812666,0.0002152861,0.0001176816],"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":[0.00370221,0.0001956009,0.000890839,0.0001018388,0.00004249648,0.00001257195,0.000755367,0.000002710475,0.0005158886,0.8883009,0.00005097053,0.1054286],"study_design_scores_gemma":[0.0008977915,0.0005672832,0.01457896,0.0002591437,0.0001019146,0.000006756636,0.0008159623,0.0002847685,0.001931829,0.9802598,0.00007357014,0.0002222171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00529848,0.0002823391,0.9919338,0.00002940859,0.00006276274,0.0004383066,0.0002743682,0.00003747718,0.001643049],"genre_scores_gemma":[0.1849487,0.0001716476,0.8146583,0.00005111899,0.00001466507,0.00003256399,0.000005130308,0.00002680188,0.00009113538],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3192216,"threshold_uncertainty_score":0.9907303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6157867327055884,"score_gpt":0.6441146613557368,"score_spread":0.02832792865014844,"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."}}