Comparison of MLE and REMLE of Linear Mixed Models in Assessing Bioequivalence based on 2x2 Crossover Design with Missing data
- Authors
- 정윤노; 박상규
- Issue Date
- Dec-2008
- Publisher
- 한국데이터정보과학회
- Keywords
- 2x2 crossover design; Bioequivalence; LMM; Missing data; MLE; REMLE.
- Citation
- 한국데이터정보과학회지, v.19, no.4, pp 1211 - 1218
- Pages
- 8
- Journal Title
- 한국데이터정보과학회지
- Volume
- 19
- Number
- 4
- Start Page
- 1211
- End Page
- 1218
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/31567
- ISSN
- 1598-9402
- Abstract
- Maximum likelihood estimator (MLE) and restricted maximum likelihood estimator (REMLE) approaches are available in analyzing the linear mixed model (LMM) like bioequivalence trials. US FDA (2001) guides that REMLE may be useful to assess bioequivalence (BE) test. This paper studies the statistical behaviors of the methods in assessing BE tests when some of observations are missing at random. The simulation results show that the REMLE maintains the given nominal level well and the MLE gives a bit higher power. Considering the levels and the powers, the REMLE approach is recommended when the sample sizes are small to moderate and the MLE approach should be used when the sample size is greater than 30.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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