{"id":"W2032234757","doi":"10.1198/jasa.2010.tm08551","title":"Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random","year":2010,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariate; Missing data; Estimator; Statistics; Estimating equations; Econometrics; Mathematics; Random effects model; Generalized estimating equation; Computer science; Meta-analysis; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003264307,0.0001396099,0.0005305505,0.00005460925,0.0003727172,0.0001189376,0.0002706059,0.00005534134,0.00005607789],"category_scores_gemma":[0.04778931,0.00009153788,0.00007547058,0.0001539219,0.0001699793,0.0001299761,0.0001433458,0.0003649281,0.000001472845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001611026,"about_ca_system_score_gemma":0.00009607624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002606478,"about_ca_topic_score_gemma":0.00003786804,"domain_scores_codex":[0.997834,0.000736768,0.0005550971,0.0001975656,0.0004384855,0.0002380505],"domain_scores_gemma":[0.9762046,0.02091345,0.002128236,0.0003047232,0.0003172019,0.0001318274],"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":[0.02567477,0.0009028597,0.3126845,0.0004006273,0.002047371,0.00008662557,0.0008717208,0.00001798263,0.05084663,0.1586544,0.1218997,0.3259128],"study_design_scores_gemma":[0.00347405,0.0002743621,0.3178504,0.0001258314,0.0009579451,0.0001374308,0.0001326097,0.08624647,0.0002397046,0.5890059,0.001265365,0.0002899577],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1545544,0.000008321115,0.8412741,0.00279685,0.0005120194,0.0001553277,0.0006748457,0.00001097983,0.00001317013],"genre_scores_gemma":[0.1306067,0.00000332493,0.8688933,0.0001230651,0.0002386257,0.000004715508,0.000008762033,0.00001738136,0.0001041311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4303515,"threshold_uncertainty_score":0.9602315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1074271845088165,"score_gpt":0.4037622792092244,"score_spread":0.2963350947004079,"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."}}