• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.553-566
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper considers design-based estimators of the parameters of these variance functions based on the within-unit sample variances. This paper devotes to: (1) define an error scale factor $\delta$; (2) develop estimators of the parameters of the linear measurement error variance function of the true values under large-sample and small-error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U.S. Third National Health and Nutrition Examination Survey (NHANES III).

• Journal of Integrative Natural Science
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• v.5 no.3
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• pp.157-162
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• 2012

Measurement error is one of main source of error in survey. It is generally defined as the difference between an observed value and an underlying true value. An observed value with error may be expressed as a function of the true value plus error term. In some cases, the measurement error variance may be also a function of the unknown true value. The error variance function can be rewritten as a function of true value multiplied by a scale factor. This research explore methods for estimation of the measurement error variance based on the data from complex sampling design. We consider the case in which the variance of mesurement error is a linear function of unknown true value, and the error variance scale factor is small. We applied our results to the U.S. Third National Health and Nutrition Examination Survey (the U.S. NHANES III) data for empirical analyses, which has replicate measurements for relatively small subset of initial respondents's group.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.1-14
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• 2003

In man cases, the measurement error variances may be functions of the unknown true values or related covariate. In some cases, the measurement error variances increase in proportion to the value of predictor. This paper develops estimators of the parameters of a linear measurement error variance function under stratified multistage random sampling design and additional conditions. Also, this paper evaluates and compares the power of an asymptotically unbiased test with that of an asymptotically biased test. The proposed method are applied to blood sample measurements from the U.S. Third National Health and Nutrition Examination Survey(NHANES III)

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.34-41
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper develops estimators of the parameters of a linear measurement error variance function based on wi thin-unit sample variaoces. This paper devotes to: (1) define measurement error scale factor $\delta$: (2) develop estimators of the parameters of the 1inear measurement error variance function under stratified multistage sampling design and small error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U S Third National Health and Nutrition Examination Survey(NHANES III)

• Clinical and Molecular Hepatology
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• v.24 no.4
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• pp.392-401
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• 2018

Background/Aims: Leptin is associated with metabolic disorders, which predispose one to non-alcoholic fatty liver disease (NAFLD). The role of leptin in NAFLD pathogenesis is not fully understood. We aim to investigate the association between serum leptin level and severity of NAFLD using U.S. nationally representative data. Methods: Data were obtained from the United States Third National Health and Nutrition Examination Survey. NAFLD was defined by ultrasound detection and severity of hepatic steatosis in the absence of other liver diseases. The severity of hepatic fibrosis was determined by NAFLD fibrosis score (NFS). We used multivariate survey-weighted generalized logistic regression to evaluate the association between leptin level and the degree of NAFLD. We also performed subgroup analyses by body mass index (lean vs. classic NAFLD). Results: Among 4,571 people, 1,610 (35%) had NAFLD. By ultrasound findings, there were 621 people with mild, 664 with moderate, and 325 with severe steatosis. There were 885 people with low NFS (<-1.455, no significant fibrosis), 596 with intermediate NFS, and 129 with high NFS (>0.676, advanced fibrosis). Leptin levels for normal, mild, moderate and severe steatosis were $10.7{\pm}0.3ng/mL$, $12.1{\pm}0.7ng/mL$, $15.6{\pm}0.8ng/mL$, $16{\pm}1.0ng/mL$, respectively (trend P-value<0.001). Leptin levels for low, intermediate, and high NFS were $11.8{\pm}0.5ng/mL$, $15.6{\pm}0.8ng/mL$, $28.5{\pm}3.5ng/mL$, respectively (trend P-value<0.001). This association remained significant even after adjusting for known demographic and metabolic risk factors. In the subgroup analysis, this association was only prominent in classic NAFLD, but not in lean NAFLD. Conclusions: Serum leptin level is associated with the severity of NAFLD, especially in classic NAFLD patients.