{"id":"W1533866157","doi":"10.1002/cjs.11257","title":"Statistical inference for the additive hazards model under outcome‐dependent sampling","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Environmental Health Sciences; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Outcome (game theory); Inference; Statistical inference; Statistical model; Statistics; Computer science; Proportional hazards model; Econometrics; Mathematics; Artificial intelligence; Mathematical economics","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":[],"consensus_categories":[],"category_scores_codex":[0.09518401,0.001276331,0.002371443,0.002226296,0.000739339,0.001531441,0.003161155,0.002045919,0.003525428],"category_scores_gemma":[0.2221659,0.000778781,0.002358986,0.002336066,0.003327799,0.002284799,0.002624475,0.002616897,0.0004504102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426359,"about_ca_system_score_gemma":0.003281225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001774745,"about_ca_topic_score_gemma":0.001183877,"domain_scores_codex":[0.8886498,0.09678949,0.002561039,0.004537489,0.006624219,0.0008378546],"domain_scores_gemma":[0.8427442,0.1326933,0.008183758,0.01195588,0.00385843,0.0005645966],"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":[0.0008327133,0.0003421442,0.02896287,0.001218715,0.001627068,0.0004424521,0.0006776701,0.1078387,0.002304596,0.6610292,0.002852926,0.191871],"study_design_scores_gemma":[0.0007670429,0.001042673,0.00784439,0.0002230141,0.0005053662,0.0002933859,0.0001402319,0.4377828,0.002245921,0.5442665,0.004811393,0.00007736677],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007135061,0.0001488934,0.9916379,0.000225954,0.00004647322,0.0002939793,0.0001445039,0.00006175607,0.0003055706],"genre_scores_gemma":[0.2309901,0.0007728458,0.7611446,0.000598389,0.0001930439,0.004228155,0.000808328,0.00005344085,0.001210975],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09518401,"threshold_uncertainty_score":0.5033872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.331872792837462,"score_gpt":0.4301216560181521,"score_spread":0.0982488631806901,"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."}}