{"id":"W4285727872","doi":"10.1186/s12874-022-01671-0","title":"The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation","year":2022,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Imputation (statistics); Missing data; Logistic regression; Statistics; Sample size determination; Standard error; Confidence interval; Odds ratio; Standard deviation; Regression analysis; Regression; Mathematics; Econometrics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04307513,0.0001009844,0.0005142501,0.0001202107,0.0002302408,0.000004487268,0.001207985,0.0001014076,0.00008210119],"category_scores_gemma":[0.4626964,0.00005302326,0.00006217004,0.000366848,0.001263007,0.00002775746,0.001144933,0.000516303,1.51492e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000545993,"about_ca_system_score_gemma":0.0006066512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002108627,"about_ca_topic_score_gemma":0.000009487339,"domain_scores_codex":[0.9675858,0.02854284,0.0008865661,0.0003246916,0.002359055,0.0003010413],"domain_scores_gemma":[0.7662141,0.2318924,0.0006111344,0.0009373021,0.0002644834,0.0000805268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003447552,0.000515435,0.002067113,0.009433311,0.00007689449,0.000005522563,0.001202014,0.02675656,0.01560438,0.1585354,0.0002975815,0.7820582],"study_design_scores_gemma":[0.0003823096,0.0004395906,0.0004144252,0.0004622186,0.0000379504,0.000004209525,0.00005097299,0.6871997,0.008083245,0.3028949,6.698261e-7,0.00002975277],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1235876,0.00007886732,0.8755365,0.00009537364,0.0001041625,0.0004548171,0.0001231765,0.000004076267,0.00001541987],"genre_scores_gemma":[0.4769685,0.000007758658,0.5229844,0.000002883658,0.000007183493,0.00001239012,0.000003709184,0.000006991283,0.000006183712],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7820285,"threshold_uncertainty_score":0.9853555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6698292803895878,"score_gpt":0.6058880685071995,"score_spread":0.06394121188238833,"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."}}