Using bootstrap method to evaluate the estimates of nicotine equivalents from linear mixed model and generalized estimating equation

Autor:	Qiwei Liang, Huajiang Li, Paul Mendes, Kim Frost-Pineda, Hans J. Roethig
Rok vydání:	2009
Předmět:	Statistics and Probability Mixed model Percentile information science Repeated measures design health care quality access and evaluation humanities Confidence interval Generalized linear mixed model Correlation Nicotine Statistics Econometrics medicine natural sciences Statistics Probability and Uncertainty Generalized estimating equation Mathematics medicine.drug
Zdroj:	Journal of Applied Statistics. 36:453-463
ISSN:	1360-0532 0266-4763
DOI:	10.1080/02664760802638074
Popis:	Twenty-four-hour urinary excretion of nicotine equivalents, a biomarker for exposure to cigarette smoke, has been widely used in biomedical studies in recent years. Its accurate estimate is important for examining human exposure to tobacco smoke. The objective of this article is to compare the bootstrap confidence intervals of nicotine equivalents with the standard confidence intervals derived from linear mixed model (LMM) and generalized estimation equation. We use percentile bootstrap method because it has practical value for real-life application and it works well with nicotine data. To preserve the within-subject correlation of nicotine equivalents between repeated measures, we bootstrap the repeated measures of each subject as a vector. The results indicate that the bootstrapped estimates in most cases give better estimates than the LMM and generalized estimation equation without bootstrap.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::434962b320926c006c448404b53c1b19 https://doi.org/10.1080/02664760802638074 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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