{"id":"W2749887430","doi":"10.1177/0272989x17725748","title":"Conducting EQ-5D Valuation Studies in Resource-Constrained Countries: The Potential Use of Shrinkage Estimators to Reduce Sample Size","year":2017,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Canadian Centre for Applied Research in Cancer Control; Institute for Clinical Evaluative Sciences; Hospital for Sick Children; Public Health Ontario; University of Toronto; SickKids Foundation; Sunnybrook Health Science Centre","funders":"","keywords":"Estimator; Shrinkage estimator; Mean squared error; Statistics; Standard error; Shrinkage; Mathematics; Econometrics; Sample size determination; Valuation (finance); Efficient estimator; Economics","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":["metaresearch"],"category_scores_codex":[0.03403508,0.0001915237,0.0009807043,0.000272338,0.0006554595,0.0002245454,0.0007398024,0.0001839805,0.000550228],"category_scores_gemma":[0.3547785,0.0001790779,0.0001149083,0.000171483,0.0004279963,0.0005049908,0.0003395798,0.0002959675,0.0001681121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003440693,"about_ca_system_score_gemma":0.00022685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005110126,"about_ca_topic_score_gemma":0.0002159902,"domain_scores_codex":[0.9941657,0.0004222264,0.00391436,0.0005699543,0.0005155919,0.0004121501],"domain_scores_gemma":[0.9694431,0.0259652,0.002992876,0.001185245,0.0001927968,0.0002207748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001178922,0.0007504379,0.3662907,0.002198642,0.001007501,0.0001660921,0.06708428,0.03575221,0.0001461111,0.2316544,0.1307912,0.1629796],"study_design_scores_gemma":[0.007872745,0.0004908873,0.3324829,0.010222,0.00008732349,0.0001007261,0.02671952,0.2373996,0.00009527193,0.2872294,0.09534958,0.001950047],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9182126,0.0005083212,0.04979187,0.02899638,0.001316023,0.0007838852,0.0001049455,0.00002893461,0.0002570538],"genre_scores_gemma":[0.9688909,0.00007148888,0.02531269,0.005260107,0.0003232545,0.00005236359,0.000003286975,0.00002484761,0.00006108468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3207434,"threshold_uncertainty_score":0.9946641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6963428736042208,"score_gpt":0.5128158556935778,"score_spread":0.183527017910643,"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."}}