{"id":"W2471532197","doi":"10.1111/biom.12561","title":"Modeling of Successive Cancer Risks in Lynch Syndrome Families in the Presence of Competing Risks Using Copulas","year":2016,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic factors in colorectal cancer","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Western University","funders":"National Cancer Institute; Canadian Institutes of Health Research; National Institutes of Health; National Center for Chronic Disease Prevention and Health Promotion; Mayo Clinic","keywords":"Penetrance; Inference; Covariate; Computer science; Statistics; Missing data; Colorectal cancer; Copula (linguistics); Selection (genetic algorithm); Causal inference; Econometrics; Medicine; Cancer; Mathematics; Internal medicine; Artificial intelligence; Biology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01271681,0.001600429,0.002243597,0.001563725,0.0007312357,0.002415643,0.003896053,0.002181598,0.002503948],"category_scores_gemma":[0.02572376,0.001733308,0.002750706,0.001831246,0.001828027,0.002214068,0.002637241,0.003636901,0.0004722765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001365456,"about_ca_system_score_gemma":0.001881089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01673167,"about_ca_topic_score_gemma":0.008573475,"domain_scores_codex":[0.9960734,0.00230971,0.0001779603,0.0007408434,0.0003405383,0.0003575065],"domain_scores_gemma":[0.974611,0.02122225,0.002264461,0.0007321595,0.0007621936,0.000407823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008429014,0.0000629253,0.01424907,0.0001151071,0.0004276375,0.001103171,0.0004785309,0.8732118,0.0006414621,0.1008814,0.0007119969,0.008032566],"study_design_scores_gemma":[0.00001699481,0.00003632421,0.001389449,0.00001902275,0.0000846321,0.000136458,0.0000451415,0.9748197,0.0001078685,0.02287662,0.0004429808,0.00002480273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06081193,0.0006793293,0.9363242,0.0005547951,0.00004576536,0.00007747681,0.0003653967,0.0001971211,0.0009439642],"genre_scores_gemma":[0.840641,0.002010035,0.1489209,0.0002851064,0.0001878942,0.0005833757,0.0009205001,0.0002072115,0.006243959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01673167,"threshold_uncertainty_score":0.06725377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1691318257472267,"score_gpt":0.3950505188103593,"score_spread":0.2259186930631326,"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."}}