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Article: Efficient estimation of log-normal means with application to pharmacokinetic data

TitleEfficient estimation of log-normal means with application to pharmacokinetic data
Authors
KeywordsArithmetic mean
Maximum likelihood
Parametric bootstrap
Squared error risk
Uniformly minimum variance unbiased
Clinical pharmacokinetics
Issue Date2006
Citation
Statistics in Medicine, 2006, v. 25, n. 17, p. 3023-3038 How to Cite?
AbstractIn this paper, the problem of interest is efficient estimation of log-normal means. Several existing estimators are reviewed first, including the sample mean, the maximum likelihood estimator, the uniformly minimum variance unbiased estimator and a conditional minimal mean squared error estimator. A new estimator is then proposed, and we show that it improves over the existing estimators in terms of squared error risk. The improvement is more significant with small sample sizes and large coefficient of variations, which is common in clinical pharmacokinetic (PK) studies. In addition, the new estimator is very easy to implement, and provides us with a simple alternative to summarize PK data, which are usually modelled by log-normal distributions. We also propose a parametric bootstrap confidence interval for log-normal means around the new estimator and illustrate its nice coverage property with a simulation study. Our estimator is compared with the existing ones via theoretical calculations and applications to real PK studies. Copyright © 2005 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/219514
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811

 

DC FieldValueLanguage
dc.contributor.authorShen, Haipeng-
dc.contributor.authorBrown, Lawrence D.-
dc.contributor.authorZhi, Hui-
dc.date.accessioned2015-09-23T02:57:16Z-
dc.date.available2015-09-23T02:57:16Z-
dc.date.issued2006-
dc.identifier.citationStatistics in Medicine, 2006, v. 25, n. 17, p. 3023-3038-
dc.identifier.issn0277-6715-
dc.identifier.urihttp://hdl.handle.net/10722/219514-
dc.description.abstractIn this paper, the problem of interest is efficient estimation of log-normal means. Several existing estimators are reviewed first, including the sample mean, the maximum likelihood estimator, the uniformly minimum variance unbiased estimator and a conditional minimal mean squared error estimator. A new estimator is then proposed, and we show that it improves over the existing estimators in terms of squared error risk. The improvement is more significant with small sample sizes and large coefficient of variations, which is common in clinical pharmacokinetic (PK) studies. In addition, the new estimator is very easy to implement, and provides us with a simple alternative to summarize PK data, which are usually modelled by log-normal distributions. We also propose a parametric bootstrap confidence interval for log-normal means around the new estimator and illustrate its nice coverage property with a simulation study. Our estimator is compared with the existing ones via theoretical calculations and applications to real PK studies. Copyright © 2005 John Wiley & Sons, Ltd.-
dc.languageeng-
dc.relation.ispartofStatistics in Medicine-
dc.subjectArithmetic mean-
dc.subjectMaximum likelihood-
dc.subjectParametric bootstrap-
dc.subjectSquared error risk-
dc.subjectUniformly minimum variance unbiased-
dc.subjectClinical pharmacokinetics-
dc.titleEfficient estimation of log-normal means with application to pharmacokinetic data-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1002/sim.2456-
dc.identifier.pmid16345103-
dc.identifier.scopuseid_2-s2.0-33747609550-
dc.identifier.volume25-
dc.identifier.issue17-
dc.identifier.spage3023-
dc.identifier.epage3038-
dc.identifier.eissn1097-0258-

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