Evaluation of techniques for handling missing cost-to-charge ratios in the USA Nationwide Inpatient Sample: a simulation study
Author(s) -
Tzy-Chyi Yu,
Huanxue Zhou
Publication year - 2015
Publication title -
journal of comparative effectiveness research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.567
H-Index - 23
eISSN - 2042-6313
pISSN - 2042-6305
DOI - 10.2217/cer.15.28
Subject(s) - missing data , imputation (statistics) , medicine , healthcare cost and utilization project , statistics , cost database , mean squared error , sample size determination , sample (material) , cost estimate , econometrics , health care , computer science , mathematics , engineering , systems engineering , chemistry , chromatography , database , economics , economic growth
Aim: Evaluate performance of techniques used to handle missing cost-to-charge ratio (CCR) data in the USA Healthcare Cost and Utilization Project's Nationwide Inpatient Sample. Methods: Four techniques to replace missing CCR data were evaluated: deleting discharges with missing CCRs (complete case analysis), reweighting as recommended by Healthcare Cost and Utilization Project, reweighting by adjustment cells and hot deck imputation by adjustment cells. Bias and root mean squared error of these techniques on hospital cost were evaluated in five disease cohorts. Results & conclusion: Similar mean cost estimates would be obtained with any of the four techniques when the percentage of missing data is low (<10%). When total cost is the outcome of interest, a reweighting technique to avoid underestimation from dropping observations with missing data should be adopted.
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