{"id":"W3004002562","doi":"10.1002/sim.8465","title":"Bayesian hierarchical meta‐analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council Canada; Medical Research Council; National Institute for Health and Care Research","keywords":"Surrogate endpoint; Bayesian probability; Computer science; Surrogate model; Surrogate data; Bayesian inference; Meta-analysis; Class (philosophy); Econometrics; Statistics; Data mining; Artificial intelligence; Machine learning; Mathematics; Medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01245903,0.0004960026,0.002296345,0.0001219572,0.0002737233,0.00005232618,0.000740023,0.0002567014,0.0002142447],"category_scores_gemma":[0.2451172,0.0003682668,0.0001570774,0.0005248212,0.0004835854,0.0001382271,0.0003290736,0.0007836361,0.000004271891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717531,"about_ca_system_score_gemma":0.0001976552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001037744,"about_ca_topic_score_gemma":0.0001061785,"domain_scores_codex":[0.9919319,0.003598186,0.002038224,0.00112474,0.0005762012,0.0007307691],"domain_scores_gemma":[0.8525165,0.1451962,0.0005010422,0.001137079,0.0001840895,0.0004651322],"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":[0.002102368,0.0009072746,0.001015017,0.002946619,0.00997664,0.0005211566,0.007840617,0.008763543,0.0005553112,0.8215508,0.003402057,0.1404186],"study_design_scores_gemma":[0.001525443,0.000223541,0.00000794793,0.0000995041,0.002820168,0.000005100099,0.0002582509,0.5026557,0.00003854806,0.4919971,0.0001868248,0.0001818319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003244687,0.0006120487,0.991186,0.00304984,0.00048003,0.001240203,0.002883537,0.00009363835,0.0001302314],"genre_scores_gemma":[0.01582179,0.0003119326,0.9824257,0.0004359126,0.0005712519,0.00008307827,0.0002025862,0.0001054693,0.00004227394],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4938922,"threshold_uncertainty_score":0.9998769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8988886626769544,"score_gpt":0.6656536011715628,"score_spread":0.2332350615053916,"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."}}