Equivalence criteria for the safety evaluation of a genetically modified crop: a statistical perspective

Abstract

SUMMARYSafety evaluation of a genetically modified (GM) crop is accomplished by establishing its substantial equivalence to non-GM reference crops with a history of safe use. Testing hypotheses of equivalence rather than difference is the appropriate statistical approach. A necessary first step in this regard is to specify a reasonable equivalence criterion that includes a measure for discrepancy between the GM and reference crops as well as a regulatory threshold. The present work explored several equivalence criteria and discussed their pros and cons. Each criterion addresses one of three ordered classes of equivalence: super, conditional and marginal equivalence. Their implications were investigated over an array of parameter values estimated from a real-world dataset. Marginal equivalence was identified as adhering most closely to the concept of substantial equivalence. Because conditional equivalence logically implies marginal equivalence and is practically quantifiable from current field designs, the present work recommends conditional equivalence criteria while encouraging producers to improve their design to enable testing marginal equivalence in the future. Contrary to concerns of the ag-biotech industry, empirical evidence from recent publications indicates that a linear mixed model currently implemented by the European Food Safety Authority is adequate for assessing equivalence despite its lack of genotype-by-environment interaction terms.

Equivalence analysis to support environmental safety assessment: Using nontarget organism count data from field trials with cisgenically modified potato

“…Rejection of this nonequivalence hypothesis implies that the difference is smaller than the LOC, and this can be considered as a proof of safety (Bross, ; Hothorn & Oberdoerfer, ; Millard, ; Perry et al, ). The advantages of using the equivalence concept for safety assessment have been described before (e.g., Perry et al, ; van der Voet, Perry, Amzal, & Paoletti, ; Meyners, ; Kang & Vahl, ; Goedhart, Voet, Baldacchino, & Arpaia, ; Vahl & Kang, ). A crucial argument in favor of equivalence testing is that the onus to do high‐quality, well‐replicated experiments with sufficient statistical power is placed on to those who wish to demonstrate the safety of GMOs (Perry et al, ).…”

G-TwYST harmonisation of statistical methods for use of omics data in food safety assessment

“…a GMO) and a set of reference varieties that have a history of safe use as foods. Traditionally, equivalence in such comparative studies for some relevant endpoint was established by testing statistically whether the means of the reference ( ) and GMO-group ( ) can be assumed to be the same or that they are found to be significantly different [van der Voet et al 2011; Vahl and Kang 2016]. The tested hypotheses for this difference test are:…”

“…The finding of a statistically significant difference by the t-test can be used to argue that more experiments are required to assess the safety of the GMO. Traditionally, non-rejection of the null-hypothesis of 'no difference' is taken to mean that there is no relevant difference between the GMO and references for that endpoint and that this endpoint on its own provides no trigger for a further safety evaluation [van der Voet 2011 et al; Vahl and Kang 2016].…”

“…Most notably, non-significance is not sufficient evidence for the null hypothesis (absence of evidence is not evidence of absence) [Altman and Bland 1995]. In other words, the approach of declaring equivalence between the GMO and references when the null-hypothesis is not rejected is flawed [Van der Voet et al 2011; Vahl and Kang 2016]. Additionally, the t-test may declare that a significant difference exists even though its magnitude is of no practical relevance [Limentani et al 2005].…”

“…Similar to the ttest, often equal variances of the GMO and reference distributions are assumed. Significance of the TOST does indeed provide evidence of the similarity of the GMO and references for the tested endpoint [Van der Voet et al 2011; Vahl and Kang 2016]. Currently, experts find it difficult to specify the equivalence limit Δ.…”