{"id":"W4410784164","doi":"10.1186/s12874-025-02594-2","title":"Comparison of methods to handle missing values in a continuous index test in a diagnostic accuracy study – a simulation study","year":2025,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Universitätsklinikum Hamburg-Eppendorf; Deutsche Forschungsgemeinschaft","keywords":"Missing data; Statistics; Covariate; Imputation (statistics); Sample size determination; Inverse probability weighting; Weighting; Correlation; Mathematics; Medicine; Estimator","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":[0.09983845,0.001318254,0.001997775,0.003082852,0.000713476,0.001642105,0.002960096,0.002860131,0.002628444],"category_scores_gemma":[0.2356104,0.0009817128,0.004584099,0.002139269,0.00121874,0.002079597,0.001788462,0.002687409,0.0002593298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002578607,"about_ca_system_score_gemma":0.002652802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007539819,"about_ca_topic_score_gemma":0.003670219,"domain_scores_codex":[0.9439428,0.05095309,0.001475019,0.00143702,0.001645696,0.0005463748],"domain_scores_gemma":[0.4069087,0.5653571,0.008363576,0.009452133,0.00849014,0.001428287],"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":[0.008290622,0.001077935,0.06996684,0.001795886,0.00422796,0.0004243614,0.0008736577,0.8241999,0.0006224973,0.01334753,0.002226871,0.07294586],"study_design_scores_gemma":[0.001195748,0.001390329,0.006841097,0.0004298311,0.0007294295,0.000275295,0.0001325888,0.9796383,0.0006362246,0.007641908,0.0009994044,0.00008989167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4859695,0.007046556,0.496648,0.001758364,0.0002971615,0.002755679,0.001062376,0.0005706908,0.003891723],"genre_scores_gemma":[0.8248186,0.00130059,0.169355,0.0003881671,0.000110795,0.002792678,0.000747742,0.00009093204,0.0003954717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9001616,"threshold_uncertainty_score":0.5280025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6339960035583913,"score_gpt":0.6978124382894738,"score_spread":0.06381643473108256,"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."}}