SEMINARS
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Fall 2007
STATISTICS
COLLOQUIUM
Wednesday, September 19, 2007
3:30-4:00—Refreshments, Yost 327
4:00-5:00—Talk,
Yost Hall, Room 101
Liang Li, PhD
Assistant Staff of Biostatistics
Dept of Quantitative Health Sciences, Cleveland Clinic
Ph.D. Statistics (2003), University of Wisconsin-Madison
Professor, Institut fur Mathematische Stochastik
Georg-August-Universitat Gottingen, Germany Varying Coefficients Model with Measurement Error
We propose a semi-parametric partially varying coefficient model to study the relationship between serum creatinine concentration and the glomerular filtration rate (GFR) among kidney donors and patients with chronic kidney disease. A regression model is used to relate serum creatinine to GFR and demographic factors in which coefficient of GFR is expressed as a function of age to allow its effect to be age-dependent. GFR measurements obtained from the clearance of a radioactively labeled isotope are assumed to be a surrogate for the true GFR, with the relationship between measured and true GFR expressed using an additive error model. We use locally corrected score equations to estimate parameters and coefficient functions, and propose an expected generalized cross-validation (EGCV) method to select the kernel bandwidth. The performance of the proposed methods, which avoid distributional assumptions on the true GFR and residuals, is investigated by simulation. Accounting for measurement error using the proposed model reduced apparent inconsistencies in the relationship between serum creatinine and GFR among different clinical data sets derived from kidney donor and chronic kidney disease source populations.
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