{"id":"W2982693598","doi":"10.1002/sim.8399","title":"A review of the use of time‐varying covariates in the Fine‐Gray subdistribution hazard competing risk regression model","year":2019,"lang":"en","type":"review","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; International Council for the Exploration of the Sea; Ontario Ministry of Health and Long-Term Care; Heart and Stroke Foundation of Canada","keywords":"Covariate; Proportional hazards model; Statistics; Gray (unit); Regression analysis; Econometrics; Regression; Mathematics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005033984,0.0003621984,0.002621033,0.0001206777,0.00004920385,0.000008172225,0.0005708532,0.0001738443,0.0001088691],"category_scores_gemma":[0.06329907,0.0001648509,0.0001374415,0.0006725626,0.0003171012,0.00002620911,0.0001516632,0.0009437075,0.000003697644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009066478,"about_ca_system_score_gemma":0.0002494781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000157985,"about_ca_topic_score_gemma":0.00003279379,"domain_scores_codex":[0.9943098,0.002422311,0.002085901,0.000301069,0.0006214374,0.0002594253],"domain_scores_gemma":[0.9728578,0.02399315,0.002049703,0.0008162266,0.0002486029,0.00003445812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00001705498,0.0001364625,0.00006668438,0.3164381,0.00006626635,0.000008777268,0.000507909,0.00003818265,0.000002339761,0.2077515,0.01346192,0.4615048],"study_design_scores_gemma":[0.0004974267,0.0001307349,0.00003361755,0.7757645,0.001927356,0.00001310717,0.00002796713,0.03811998,0.000001052028,0.15772,0.02546721,0.000297046],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001976419,0.6782525,0.3179381,0.00005339064,0.0001196164,0.001173251,0.002311844,0.000005204232,0.0001441491],"genre_scores_gemma":[0.000008119365,0.7926552,0.2069769,0.00006743996,0.00003109814,0.00003392589,0.0001809307,0.00002380623,0.00002253241],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.4612077,"threshold_uncertainty_score":0.9445912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2756750443033474,"score_gpt":0.4708426292700219,"score_spread":0.1951675849666745,"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."}}