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Activity Number:
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425
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #301983 |
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Title:
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Comparison of Statistical Summary Measures of ELISPOT Data
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Author(s):
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Chan Zeng*+ and Samantha MaWhinney and Elizabeth McFarland and Anna E. Baron
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Companies:
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University of Colorado and University of Colorado and University of Colorado and University of Colorado
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Address:
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17213 E. Lake Dr., Aurora, CO, 80016-3208,
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Keywords:
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ELISPOT assay ; NLMIXED model ; HIV/AIDS
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Abstract:
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The ELISPOT assay is a commonly used technique for quantifying the occurrence of T lymphocyte cells secreting a cytokine after stimulation with an antigen or peptide. The assay endpoint, the number of spot-forming cells at a specific concentration of effector cells, is typically estimated using either a simple mean or the predicted value from a linear regression model. We compare modeling approaches using HIV/AIDS data. A simulation study was conducted to compare methods under controlled conditions. We demonstrate that the simple mean is appropriate only if the assays are conducted at the same concentration for all samples. "Normalizing" simple means to the optimal concentration using results from a range of concentrations is not valid. A random effects or mixed model is superior to the simple mean when a large within-assay variance relative to between-subject variance exists. When assays are conducted over a range of effector cell concentrations for each individual, the linearity assumption is often violated. In this case, nonlinear models provide more accurate estimation. Collecting data over a range of concentrations allows reassessment of the optimal cell concentration.
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