{"id":"W3026103209","doi":"10.1016/j.csda.2020.107007","title":"Competing risk modeling and testing for X-chromosome genetic association","year":2020,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; Hong Kong Polytechnic University; Research Grants Council, University Grants Committee; National Natural Science Foundation of China; University of International Business and Economics","keywords":"Population; Skewness; Genetic association; Chromosome; Asymptotic distribution; Statistics; Multiple comparisons problem; Genetic model; Mathematics; Computer science; Econometrics; Genetics; Biology; Estimator; Medicine; Single-nucleotide polymorphism; Genotype","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":[0.07503192,0.001990983,0.004060159,0.002877326,0.001547589,0.003520731,0.00790783,0.004042166,0.008258598],"category_scores_gemma":[0.1873648,0.002013877,0.005603257,0.003449461,0.002847226,0.002822301,0.003536168,0.005500185,0.001156527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001672784,"about_ca_system_score_gemma":0.005511877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01110195,"about_ca_topic_score_gemma":0.00727492,"domain_scores_codex":[0.9229609,0.06765824,0.002058144,0.004164536,0.002141073,0.001017086],"domain_scores_gemma":[0.5946517,0.3881217,0.004265343,0.009365082,0.002312085,0.001284032],"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":[0.004329599,0.0005902118,0.05301308,0.0007887771,0.005579012,0.002387848,0.001227717,0.5562376,0.0005773789,0.2313716,0.01009401,0.1338031],"study_design_scores_gemma":[0.0001754916,0.0001631309,0.001486609,0.000034819,0.0001938913,0.0002769863,0.00006623839,0.9329859,0.0001558641,0.06348277,0.0009518928,0.00002640677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02738578,0.0006538529,0.9673552,0.001766948,0.0001835707,0.0002689828,0.001024796,0.0008372794,0.0005235284],"genre_scores_gemma":[0.4784321,0.0007225704,0.5085328,0.0007140869,0.0005106825,0.002787509,0.002529217,0.000402419,0.005368725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07503192,"threshold_uncertainty_score":0.3968115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0498749994889167,"score_gpt":0.3052781933639029,"score_spread":0.2554031938749862,"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."}}