Limits...
A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

Goedhart PW, van der Voet H, Baldacchino F, Arpaia S - Ecol Evol (2014)

Bottom Line: Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment.A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart.Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared.

View Article: PubMed Central - PubMed

Affiliation: Biometris, Plant Research International, Wageningen University and Research Centre P.O. Box 16, 6700 AA, Wageningen, The Netherlands.

ABSTRACT
Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

No MeSH data available.


95% likelihood ratio confidence intervals for the ratio of the Poisson means of the GM plant and the comparator when the underlying mean of both is μ = 5 and various numbers of replication N. The red vertical lines denote the artificial equivalence limits set at 1/2 and 2.
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fig05: 95% likelihood ratio confidence intervals for the ratio of the Poisson means of the GM plant and the comparator when the underlying mean of both is μ = 5 and various numbers of replication N. The red vertical lines denote the artificial equivalence limits set at 1/2 and 2.

Mentions: Properties of the TOST approach to equivalence testing were assessed for count data which were simulated according to the Poisson distribution. The hypothesis of non-equivalence is rejected in favor of equivalence when the confidence interval completely lies in the interval determined by fixed lower and upper equivalence limits. The same simulation setting as in the first simulation was used However, as the Poisson distribution was employed to simulate data there is no over dispersion. Hypothetical equivalence limits of ½ and 2 were employed to perform equivalence testing. A 95% likelihood ratio confidence interval for the ratio of the GMO mean and the comparator mean was calculated for each simulated dataset. The number of times this interval lies within the equivalence interval (½, 2) can then be counted. As an example, the confidence interval for 40 simulated datasets is given in Figure 5 with μ = 5 for both the GMO and the comparator, so θ = 1, and for various values of the number of replications N. In this case, the GMO and comparator have equal means and are thus theoretically equivalent. However, for small numbers of replications, the confidence intervals frequently cross the equivalence limits implying that the hypothesis of non-equivalence is not always rejected.


A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

Goedhart PW, van der Voet H, Baldacchino F, Arpaia S - Ecol Evol (2014)

95% likelihood ratio confidence intervals for the ratio of the Poisson means of the GM plant and the comparator when the underlying mean of both is μ = 5 and various numbers of replication N. The red vertical lines denote the artificial equivalence limits set at 1/2 and 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4020688&req=5

fig05: 95% likelihood ratio confidence intervals for the ratio of the Poisson means of the GM plant and the comparator when the underlying mean of both is μ = 5 and various numbers of replication N. The red vertical lines denote the artificial equivalence limits set at 1/2 and 2.
Mentions: Properties of the TOST approach to equivalence testing were assessed for count data which were simulated according to the Poisson distribution. The hypothesis of non-equivalence is rejected in favor of equivalence when the confidence interval completely lies in the interval determined by fixed lower and upper equivalence limits. The same simulation setting as in the first simulation was used However, as the Poisson distribution was employed to simulate data there is no over dispersion. Hypothetical equivalence limits of ½ and 2 were employed to perform equivalence testing. A 95% likelihood ratio confidence interval for the ratio of the GMO mean and the comparator mean was calculated for each simulated dataset. The number of times this interval lies within the equivalence interval (½, 2) can then be counted. As an example, the confidence interval for 40 simulated datasets is given in Figure 5 with μ = 5 for both the GMO and the comparator, so θ = 1, and for various values of the number of replications N. In this case, the GMO and comparator have equal means and are thus theoretically equivalent. However, for small numbers of replications, the confidence intervals frequently cross the equivalence limits implying that the hypothesis of non-equivalence is not always rejected.

Bottom Line: Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment.A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart.Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared.

View Article: PubMed Central - PubMed

Affiliation: Biometris, Plant Research International, Wageningen University and Research Centre P.O. Box 16, 6700 AA, Wageningen, The Netherlands.

ABSTRACT
Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

No MeSH data available.