{"id":"W2144168223","doi":"10.1002/sim.5984","title":"The use of propensity score methods with survival or time‐to‐event outcomes: reporting measures of effect similar to those used in randomized experiments","year":2013,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1441,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Heart and Stroke Foundation of Canada","keywords":"Propensity score matching; Observational study; Marginal structural model; Inverse probability weighting; Medicine; Covariate; Randomized controlled trial; Confounding; Average treatment effect; Statistics; Hazard ratio; Selection bias; Population; Survival analysis; Matching (statistics); Proportional hazards model; Confidence interval; Internal medicine; Mathematics","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2174702,0.004157696,0.004999245,0.01026048,0.001575541,0.008248786,0.006004136,0.008134787,0.01001388],"category_scores_gemma":[0.5252035,0.001889699,0.008818037,0.01824831,0.006105338,0.01237269,0.008749089,0.01232288,0.003443172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003038803,"about_ca_system_score_gemma":0.008339749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002994108,"about_ca_topic_score_gemma":0.001900269,"domain_scores_codex":[0.6394306,0.2924809,0.02367798,0.0108429,0.03267649,0.0008911327],"domain_scores_gemma":[0.429904,0.4315079,0.05637686,0.06826769,0.01300956,0.0009340929],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004632436,0.0002206709,0.007671661,0.01134305,0.006081732,0.0002169762,0.001714931,0.01972799,0.001002432,0.5535533,0.05548134,0.3425226],"study_design_scores_gemma":[0.0006025329,0.0006282846,0.004716466,0.005001329,0.001816547,0.0007356432,0.0003969387,0.03477466,0.003419492,0.7842777,0.1631424,0.0004880102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007856977,0.007410959,0.9824929,0.002335476,0.001091803,0.001315961,0.001651181,0.000509948,0.002405997],"genre_scores_gemma":[0.03131308,0.01598787,0.9277407,0.004357662,0.002733326,0.01266883,0.002271846,0.0008063878,0.00212038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7825298,"threshold_uncertainty_score":0.9649985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4544700176042719,"score_gpt":0.5290019207742545,"score_spread":0.0745319031699826,"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."}}