{"id":"W2961162542","doi":"10.1002/sim.8316","title":"Bayesian consensus‐based sample size criteria for binomial proportions","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Prior probability; Frequentist inference; Sample size determination; Bayesian probability; Statistics; Econometrics; Credible interval; Sample (material); Mathematics; Point estimation; Bayes' theorem; Computer science; Bayesian inference","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":[0.1353778,0.001910513,0.00422549,0.005732763,0.001820632,0.003793434,0.005676676,0.00488353,0.009482085],"category_scores_gemma":[0.4444259,0.001132202,0.002818813,0.003012084,0.005406072,0.006454331,0.005125268,0.006381859,0.002056125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002966704,"about_ca_system_score_gemma":0.003913818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001120489,"about_ca_topic_score_gemma":0.0009622111,"domain_scores_codex":[0.8813911,0.08357892,0.006062664,0.007423298,0.02017953,0.001364524],"domain_scores_gemma":[0.6058661,0.3482065,0.009350507,0.01369495,0.02101807,0.001863891],"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":[0.00107879,0.0001824726,0.004375207,0.001790632,0.0004231437,0.0002260496,0.001208008,0.07158306,0.002233431,0.6888191,0.01063062,0.2174496],"study_design_scores_gemma":[0.0005136163,0.0004617116,0.001820548,0.0009477842,0.0001766091,0.000329147,0.0002199436,0.1920331,0.003563147,0.7817197,0.01806656,0.0001481944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002738863,0.0006442278,0.9901949,0.0006780991,0.0001471091,0.0008280266,0.0001740665,0.0002088552,0.004385878],"genre_scores_gemma":[0.08269298,0.0005907317,0.9069695,0.0009392407,0.0003239064,0.006238903,0.0004314012,0.0002701744,0.001543081],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1353778,"threshold_uncertainty_score":0.7159547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1486759724768557,"score_gpt":0.5096998485410166,"score_spread":0.3610238760641609,"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."}}