{"id":"W1982616925","doi":"10.1002/cjs.11198","title":"A semiparametric linear transformation model to estimate causal effects for survival data","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Program for New Century Excellent Talents in University; National Natural Science Foundation of China; National Science Foundation","keywords":"Semiparametric regression; Semiparametric model; Estimator; Transformation (genetics); Econometrics; Mathematics; Parametric statistics; Proportional hazards model; Generalized linear model; Linear regression; Estimating equations; Linear model; Statistics; Power transform; Data transformation; Applied mathematics; Computer science; Data mining","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":[0.04041502,0.0009820833,0.001941572,0.00228159,0.0004581413,0.002179221,0.00258269,0.001596216,0.005669951],"category_scores_gemma":[0.1247704,0.0007809864,0.002323147,0.00262692,0.00274869,0.002943567,0.002418283,0.00373146,0.001137775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001281829,"about_ca_system_score_gemma":0.002401595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002737236,"about_ca_topic_score_gemma":0.001853021,"domain_scores_codex":[0.9650946,0.02994084,0.0007726857,0.001827705,0.001827379,0.0005367624],"domain_scores_gemma":[0.8554211,0.1265626,0.00600007,0.008416442,0.003075914,0.0005237987],"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":[0.0006530504,0.0003188064,0.02013864,0.0005851533,0.001157045,0.0007286846,0.001092638,0.3888301,0.001739912,0.4043096,0.005466073,0.1749802],"study_design_scores_gemma":[0.0001323975,0.0003189349,0.002207321,0.00007251583,0.0001551789,0.0003043028,0.0001215292,0.7645568,0.0006596402,0.2281459,0.003269732,0.00005585417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008122087,0.0001442267,0.990509,0.0003775455,0.00003071712,0.0001004822,0.0001635911,0.0001980662,0.0003544011],"genre_scores_gemma":[0.5948654,0.000728046,0.3960789,0.0006140783,0.0002016259,0.001745453,0.0009924752,0.0002751718,0.004498923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04041502,"threshold_uncertainty_score":0.2137376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5741567501406204,"score_gpt":0.5334148321011077,"score_spread":0.04074191803951266,"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."}}