{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002802646,0.0002033401,0.00069817,0.0003489642,0.0001202039,0.0001179258,0.0006801738,0.0001394786,0.00009211643],"category_scores_gemma":[0.1427642,0.0001809493,0.00007101266,0.0003218893,0.000087435,0.0002410549,0.00002862838,0.0003167375,0.0000336977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001513969,"about_ca_system_score_gemma":0.0010568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005068018,"about_ca_topic_score_gemma":0.002169957,"domain_scores_codex":[0.997418,0.0002327894,0.001271585,0.0002123034,0.0003677846,0.0004975366],"domain_scores_gemma":[0.9676453,0.02927239,0.0004203524,0.0004816196,0.0008637278,0.001316533],"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":[0.0002029375,0.0001703387,0.0003297965,0.001850781,0.0004624399,0.0001531751,0.001264885,0.006878308,0.0002076297,0.5606462,0.2660537,0.1617798],"study_design_scores_gemma":[0.0009620859,0.000374747,0.0002414668,0.000126833,0.0002399205,0.00001945097,0.00002872101,0.2655286,0.00009465209,0.731451,0.0007190667,0.0002134763],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003532717,0.00002340711,0.989632,0.0005323489,0.0009884916,0.0009456854,0.004169232,0.00001118696,0.0001649072],"genre_scores_gemma":[0.02300784,0.000005978734,0.9763218,0.0002189779,0.0002691215,0.00002199911,0.00002780304,0.00004920517,0.00007728169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2653346,"threshold_uncertainty_score":0.8644567,"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."}}