{"id":"W2492495737","doi":"10.1016/j.jcrc.2016.07.017","title":"Immortal time bias in observational studies of time-to-event outcomes","year":2016,"lang":"en","type":"article","venue":"Journal of Critical Care","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":180,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Observational study; Proportional hazards model; Medicine; Logistic regression; Hazard ratio; Survival analysis; Statistics; Regression analysis; Econometrics; Internal medicine; Confidence interval; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004668715,0.0001233017,0.0005218225,0.0001482012,0.00001742203,0.000006297457,0.0001740403,0.00006078814,0.0003325471],"category_scores_gemma":[0.01276616,0.00007375356,0.0001418595,0.0001067512,0.0001353036,0.0002243068,0.00007572531,0.0001326533,0.00003072825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001626244,"about_ca_system_score_gemma":0.00006926907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.32583e-7,"about_ca_topic_score_gemma":0.000002747309,"domain_scores_codex":[0.9984242,0.00007979557,0.000763859,0.00009224378,0.000462918,0.0001769922],"domain_scores_gemma":[0.9957649,0.002718354,0.0001765845,0.0001393432,0.00110907,0.00009170868],"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":[0.0009052608,0.002819919,0.1901668,0.004277844,0.0007011761,0.001216352,0.01109189,0.0001210223,0.1453086,0.5778861,0.02708752,0.03841747],"study_design_scores_gemma":[0.001358628,0.001776551,0.03744257,0.001891806,0.0002064328,0.00008601726,0.002193515,0.00003157823,0.03568683,0.9182685,0.000528015,0.0005295463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809703,0.0004846075,0.008607192,0.008621409,0.0001310344,0.0002158424,0.00006190002,0.00004387607,0.0008638497],"genre_scores_gemma":[0.9703083,0.000004811683,0.02918921,0.0001463989,0.00006016456,0.000005247878,4.316167e-7,0.00001502303,0.0002703913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3403824,"threshold_uncertainty_score":0.9955497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4412060308633293,"score_gpt":0.5311251808301917,"score_spread":0.08991914996686234,"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."}}