Interval estimation for concentration in the ELISA setting.

Enzyme-linked immunosorbent assays (ELISAs) are often used to quantify the concentration of biological substances. In a typical analysis only a point estimate of the concentration will be presented as interval estimation continues to present challenges for non-linear dose-response models. In this setting, interval estimates calculated using a Wald approach can suffer from poor coverage and have limits that fall outside parameter boundaries. Here we compare profile likelihood interval estimation procedures to Wald type intervals for the interval estimation of a concentration in the ELISA setting. Through a comprehensive simulation study, it is shown that profile likelihood methods result in interval estimates with superior coverage and that are more robust to differences in assay design when compared to Wald based approaches.

Estimation of ELISA results using a parallel curve analysis.

We introduce a new method for the analysis of enzyme-linked immunosorbent assay (ELISA) data. The new method can use data near the asymptotes and does not give undue weight to responses on the flatter parts of the dose-response curve. We apply it to simulated data and to two real-world assays and show it is more accurate and more precise than the traditional interpolation method. In particular, the new method works much better for very low-concentration samples for which the traditional method is often unable to give a result.

Parallelism in practice: approaches to parallelism in bioassays.

UNLABELLED: Relative potency bioassays are used to estimate the potency of a test biological product relative to a standard or reference product. It is established practice to assess the parallelism of the dose-response curves of the products prior to calculating relative potency. This paper provides a review of parallelism testing for bioassays. In particular three common methods for parallelism testing are reviewed: two significance tests (the F-test, the χ(2)-test) and an equivalence test. Simulation is used to compare these methods. We compare the sensitivity and specificity and receiver operating characteristic curves, and find that both the χ(2)-test and the equivalence test outperform the F-test on average, unless the assay-to-assay variation is considerable. No single method is optimal in all situations. We describe how bioassay scientists and statisticians can work together to determine the best approach for each bioassay, taking into account its properties and the context in which it is applied. LAY ABSTRACT: Bioassays are experiments that use living organisms, tissues, or cells to measure the concentration of a pharmaceutical. Typically the response of the living matter to a test sample with an unknown concentration of a pharmaceutical is compared to the response to a standard reference sample with a known concentration. An important step in the analysis of bioassays is checking that the test sample is responding like a diluted copy of the reference sample; this is known as testing for parallelism. There are three statistical methods commonly used to test for parallelism: the F-test, the χ(2)-test, and the equivalence test. This paper compares the three methods using computer simulations. We conclude that different methods are best in different situations, and we provide guidelines to help bioassay scientists and statisticians decide which method to use.