{"id":"W2013920814","doi":"10.1002/sim.4200","title":"Comparing paired vs non‐paired statistical methods of analyses when making inferences about absolute risk reductions in propensity‐score matched samples","year":2011,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":325,"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; Statistics; Confidence interval; Confounding; Sample size determination; Selection bias; Observational study; Statistical significance; Statistical inference; Matching (statistics); Causal inference; 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":[0.3921677,0.001958479,0.003827057,0.003772027,0.001552199,0.005874407,0.004651726,0.003755744,0.008487406],"category_scores_gemma":[0.7571114,0.001709421,0.007897763,0.004512876,0.006821116,0.006840642,0.004921835,0.008159158,0.001184618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002002545,"about_ca_system_score_gemma":0.003287762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001084618,"about_ca_topic_score_gemma":0.001071493,"domain_scores_codex":[0.3320736,0.6052327,0.01939249,0.01550575,0.02658703,0.001208378],"domain_scores_gemma":[0.1440299,0.7566448,0.0317837,0.05181341,0.01491351,0.0008146727],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01301471,0.002176652,0.06633946,0.01309498,0.0349429,0.001193749,0.0100308,0.04449741,0.003884831,0.2143002,0.02376537,0.5727589],"study_design_scores_gemma":[0.006752149,0.01620483,0.07463906,0.00922901,0.01476885,0.002174336,0.003535578,0.265134,0.02951462,0.5006917,0.07604178,0.001314232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02143456,0.002079766,0.9641409,0.001356372,0.001450131,0.004995408,0.0005793584,0.000549677,0.003413794],"genre_scores_gemma":[0.2332365,0.0009323264,0.7443196,0.001942047,0.0005003312,0.01689554,0.0005524331,0.0005322728,0.001088874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6078323,"threshold_uncertainty_score":0.7495655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6053372113795614,"score_gpt":0.5275261212026646,"score_spread":0.0778110901768968,"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."}}