{"id":"W1844988398","doi":"10.1002/cjs.11210","title":"Reweighting estimators for the additive hazards model with missing covariates","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Covariate; Estimator; Missing data; Statistics; Econometrics; Regression analysis; Proportional hazards model; Regression; Mathematics; Random effects model; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.03543633,0.001054887,0.001902001,0.002210069,0.0003988302,0.001241172,0.003900453,0.001832392,0.002335853],"category_scores_gemma":[0.1033217,0.0007294838,0.001611121,0.002344415,0.001365422,0.002667572,0.001807335,0.002934977,0.000682726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006792461,"about_ca_system_score_gemma":0.001348078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680166,"about_ca_topic_score_gemma":0.001269631,"domain_scores_codex":[0.9861426,0.01040117,0.0005151933,0.001163189,0.001516093,0.0002617528],"domain_scores_gemma":[0.9293123,0.05679525,0.003901103,0.006461714,0.003192665,0.000336938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004893786,0.0002222257,0.01719141,0.0009476413,0.00152573,0.0004339692,0.0005753447,0.2865976,0.005133741,0.1992551,0.005575425,0.4820524],"study_design_scores_gemma":[0.000152553,0.0003563673,0.003594757,0.0002099921,0.0002965716,0.0003318906,0.0001071518,0.7720038,0.002569441,0.210803,0.009468336,0.0001061247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004523078,0.0003778395,0.994603,0.0001094303,0.00004163845,0.00005511261,0.00005527189,0.0001195583,0.0001151388],"genre_scores_gemma":[0.1713946,0.001388852,0.8230841,0.0002418477,0.0002699622,0.0007378457,0.0005544829,0.0002190898,0.002109267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03543633,"threshold_uncertainty_score":0.1874075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04421945704352952,"score_gpt":0.3100219697863396,"score_spread":0.2658025127428101,"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."}}