{"id":"W2910522615","doi":"10.1002/sim.8503","title":"Propensity scores using missingness pattern information: a practical guide","year":2020,"lang":"en","type":"preprint","venue":"Statistics in Medicine","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health and Social Care Research and Development Division; Engineering and Physical Sciences Research Council; Economic and Social Research Council; Chief Scientist Office, Scottish Government Health and Social Care Directorate; Medical Research Council Canada; London School of Hygiene and Tropical Medicine; Medical Research Council; Public Health Agency; Department of Health and Social Care; Scottish Government; British Heart Foundation; Wellcome Trust","keywords":"Missing data; Propensity score matching; Confounding; Health records; Computer science; Statistics; Econometrics; Psychology; Medicine; Health care; Mathematics; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02685341,0.003361618,0.003007008,0.01048385,0.001153444,0.005324238,0.004703126,0.004253813,0.03203605],"category_scores_gemma":[0.1168383,0.003033568,0.002179441,0.007499553,0.003264097,0.006416871,0.004211988,0.01043712,0.01701368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001393254,"about_ca_system_score_gemma":0.004911482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003479897,"about_ca_topic_score_gemma":0.004765759,"domain_scores_codex":[0.9851847,0.01000011,0.001870928,0.0007153177,0.002118008,0.0001110249],"domain_scores_gemma":[0.9256236,0.06076942,0.002802095,0.003461046,0.006491702,0.0008521564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009635121,0.0002699898,0.001890845,0.00157188,0.0002442379,0.0007796586,0.001194378,0.01289566,0.0008150575,0.1447199,0.4130709,0.4224511],"study_design_scores_gemma":[0.0002483581,0.0001413381,0.001321936,0.001874966,0.0000828593,0.002014706,0.0006831796,0.04106501,0.0006526237,0.4696623,0.4820096,0.0002430668],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003387014,0.001753983,0.9780743,0.007825652,0.000454601,0.001144796,0.002091721,0.00308437,0.00523188],"genre_scores_gemma":[0.002569269,0.003214774,0.9863648,0.001264585,0.0005336637,0.001714323,0.001028007,0.0005603901,0.00275016],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03203605,"threshold_uncertainty_score":0.1420161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2291836310970289,"score_gpt":0.4009039550196375,"score_spread":0.1717203239226086,"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."}}