Evaluating ELISPOT summary measures with criteria for obtaining reliable estimates

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 (SFC) at a specific concentration of effector cells, is typically estimated using either a simple arithmetic mean or the predicted value from a linear regression model. We compare statistical modeling approaches for summarizing these assays using data from the Pediatric AIDS Clinical Trial Group (PACTG) study 299. A simulation study was conducted to compare methods under controlled conditions. Assuming the optimal effector cell concentration is known, we demonstrate that the simple mean is appropriate if assays are conducted at the same concentration for all samples. Normalizing simple means to a summary 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 (subject) variance relative to between-subject variance exists. When assays are conducted over a range of effector cell concentrations for each individual, the theoretical linearity assumption of the regression model is often violated and can result in biased estimates. In this case, nonlinear models provide more accurate estimation. Collecting data over a range of concentrations allows reassessment of the optimal cell concentration after the data are generated.