{"id":"W4221109862","doi":"10.1214/21-aoas1518","title":"Accounting for drop-out using inverse probability censoring weights in longitudinal clustered data with informative cluster size","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Censoring (clinical trials); Statistics; Inverse probability; Inference; Generalized estimating equation; Mathematics; Random effects model; Estimating equations; Marginal structural model; Econometrics; Causal inference; Computer science; Medicine; Estimator; Artificial intelligence; Meta-analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.002188983,0.0001707237,0.0002996118,0.00006954012,0.0003089939,0.00008107564,0.001353899,0.00003043965,0.000005280575],"category_scores_gemma":[0.0001134199,0.0001239213,0.00002566391,0.0002947747,0.0001112007,0.0003766797,0.001332475,0.0002586098,7.572377e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004006247,"about_ca_system_score_gemma":0.000153716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003195826,"about_ca_topic_score_gemma":0.00007212636,"domain_scores_codex":[0.9983383,0.0001228082,0.0004537266,0.0003634646,0.0003668781,0.0003547788],"domain_scores_gemma":[0.9975334,0.0009100044,0.0003864685,0.0009764017,0.000146895,0.0000468142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001762279,0.0003700943,0.0004511995,0.0007444011,0.0002184071,0.00001465345,0.04414371,0.02428716,0.0005661051,0.8649769,0.002477566,0.05998757],"study_design_scores_gemma":[0.0007216842,0.0000972458,0.0004831139,0.00002556991,0.00002265783,0.000006193919,0.0002587874,0.7755817,0.000337238,0.2216986,0.0005537302,0.0002135057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02162133,0.00001013617,0.9764931,0.0002346724,0.00008046503,0.0007946896,0.0003932223,0.00002099589,0.0003513717],"genre_scores_gemma":[0.2200986,0.000003189015,0.7793494,0.0004347116,0.00002892576,0.00004111768,0.00002191343,0.0000112596,0.00001090358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7512945,"threshold_uncertainty_score":0.5053365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1611153341132059,"score_gpt":0.3564482760153331,"score_spread":0.1953329419021272,"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."}}