{"id":"W4225377897","doi":"10.1002/pds.5446","title":"Alternative analytic and matching approaches for the prevalent new‐user design: A simulation study","year":2022,"lang":"en","type":"article","venue":"Pharmacoepidemiology and Drug Safety","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Janssen Pharmaceuticals; Johnson and Johnson; National Institutes of Health","keywords":"Propensity score matching; Matching (statistics); Weighting; Medicine; Statistics; Relative risk; Cohort; Efficiency; Econometrics; Mathematics; Internal medicine; Confidence interval","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1165132,0.0009206135,0.00158273,0.001402649,0.000875993,0.001929621,0.003406533,0.001849666,0.003428796],"category_scores_gemma":[0.2079706,0.0008746494,0.003841392,0.001529423,0.00138194,0.001583329,0.002216851,0.002262311,0.0002863792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002744245,"about_ca_system_score_gemma":0.003407694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007056975,"about_ca_topic_score_gemma":0.003581349,"domain_scores_codex":[0.9136361,0.08051061,0.001237195,0.002062982,0.001875928,0.0006773142],"domain_scores_gemma":[0.7344953,0.2253455,0.01491664,0.01811045,0.005682016,0.001450101],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01906742,0.002538879,0.1297391,0.001244242,0.006600288,0.0007257208,0.001434636,0.6118023,0.001034522,0.1162856,0.0032722,0.106255],"study_design_scores_gemma":[0.005876398,0.003601762,0.00684007,0.0002654211,0.001071339,0.000272093,0.0002167127,0.9369035,0.0009109137,0.04091452,0.003028603,0.00009860571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4471803,0.001278457,0.5372902,0.001819239,0.000235541,0.00672584,0.0009259212,0.0002871038,0.004257441],"genre_scores_gemma":[0.8123319,0.0003906551,0.1784085,0.0005935152,0.00007467603,0.007051145,0.0003482241,0.00003267744,0.0007687545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8834868,"threshold_uncertainty_score":0.616188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3953143985526518,"score_gpt":0.4816124235455468,"score_spread":0.08629802499289496,"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."}}