Combinability of Animal Data in Relative Potency Estimations

Abstract

In this article, we propose a strategy to show the combinability of multiple animal datasets in a parallel-line assay to estimate the relative potency. The following three assumptions are made in the linear fixed-effect modeling, and we examine if any of them result in nonconformance: The proposed procedure is demonstrated in an example analysis, and its properties are evaluated through a Monte Carlo simulation. The power of our proposed intrasubject parallelism criterion was shown to be high for designs of moderate size, but the demonstration of homogeneity via I 2 was rather conservative.

Assessment of Intrasub ject Parallelism in Ex Vivo Bioassay Using Two One-Sided Tolerance Limits

“…In an attempt to solve this problem, Uehara et al (2016) developed the ISP, an analogue of the aggregated IBE criterion proposed by the FDA. The ISP can offer a higher power than a disaggregated procedure, but this higher power can also result in ambiguity in the interpretation of results.…”

“…Applying the aforementioned method for an unbalanced one-way random effect model (Thomas and Hultquist, 1978; see also Appendix 2 of Uehara et al, 2016), it can be seen that SS l(B) and SS 2 l(W ) are approximately chi-squared distributed according to (20). In unbalanced cases, however, their mutual independence is lost.…”

“…If all the measurements were performed concurrently (e.g., at the sacrifice), then the data can be assumed to be "missing completely at random" (Little and Rubin, 2002), and we can handle the data as if they were taken from a study that was designed to be unbalanced. In this case, we may approximate the distribution of s 2 l in (13) and (14) by applying a method for unbalanced one-way random effect models (Thomas and Hultquist, 1978; see also Appendix 2 of Uehara et al, 2016) as s…”

“…Recently, Uehara et al proposed a metric for the evaluation of intrasubject parallelism in parallel-line assay ("ISP"), given as E(d 2 i )/Var (bi | bi) (Uehara et al, 2016). Here di is the difference of dose-response slopes between substances in subject i, bi is the vector of subject specific parameters (intercept and slope for each substance), bi is the subject specific slope under the control treatment, andbi is its estimate given by the least square method.…”

Evaluation of Intrasubject Parallelism in Balanced Ex Vivo Bioassay Using One-sided Efficient Score Tests of Slope Difference Quantile

“…When using multiple animals, it is also crucial to retain parallelism not only in the averages across subjects but also within each animal. Regarding this, Uehara et al (2016a) stated that "if, for any subject, the two dose-response curves cross, there the log-RP estimate does not make sense, and thus the estimate obtained by averaging all subjects becomes meaningless." So far, the issue of intrasubject parallelism has not attracted much attention, and the number of proposals to solve this problem is limited.…”