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Statistical procedures for testing hypotheses of equivalence in the safety evaluation of a genetically modified crop

Published online by Cambridge University Press:  22 January 2016

Q. KANG  and
C. I. VAHL
Q. KANG
Affiliation:
Independent Statistical Consultant, Manhattan, Kansas 66503, USA
C. I. VAHL*
Affiliation:
Department of Statistics, Kansas State University, Manhattan, Kansas 66506, USA
*
* To whom all correspondence should be addressed. Email: [email protected]
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Summary

Safety evaluation of a genetically modified crop entails assessing its equivalence to conventional crops under multi-site randomized block field designs. Despite mounting petitions for regulatory approval, there lack a scientifically sound and powerful statistical method for establishing equivalence. The current paper develops and validates two procedures for testing a recently identified class of equivalence uniquely suited to crop safety. One procedure employs the modified large sample (MLS) method; the other is based on generalized pivotal quantities (GPQs). Because both methods were originally created under balanced designs, common issues associated with incomplete and unbalanced field designs were addressed by first identifying unfulfilled theoretical assumptions and then replacing them with user-friendly approximations. Simulation indicated that the MLS procedure could be very conservative in many occasions irrespective of the balance of the design; the GPQ procedure was mildly liberal with its type I error rate near the nominal level when the design is balanced. Additional pros and cons of these two procedures are also discussed. Their utility is demonstrated in a case study using summary statistics derived from a real-world dataset.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 154 , Issue 8 , November 2016 , pp. 1392 - 1412
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Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Chiu, S. T., Tsai, P. Y. & Liu, J. P. (2010). Statistical evaluation of non-profile analyses for the in vitro bioequivalence. Journal of Chemometrics 24, 617–625.Google Scholar
Codex Alimentarius Commission (2009). Foods Derived from Modern Biotechnology, 2nd edn, Rome: Joint FAO/WHO Food Standards Programme.Google Scholar
Davit, B. M., Chen, M. L., Conner, D. P., Haidar, S. H., Kim, S., Lee, C. H., Lionberger, R. A., Maklouf, F. T., Nwakama, P. E., Patel, D. T., Schuirmann, D. J. & Yu, L. X. (2012). Implementation of a reference-scaled average bioequivalence approach for highly variable generic drug products by the US Food and Drug Administration. The AAPS Journal 14, 915–924.CrossRefGoogle ScholarPubMed
Dragalin, V., Fedorov, V., Patterson, S. & Jones, B. (2003). Kullback-Leibler divergence for evaluating bioequivalence. Statistics in Medicine 22, 913–930.Google Scholar
EFSA (2010). Scientific opinion on statistical considerations for the safety evaluation of GMOs. EFSA panel on GMOs. EFSA Journal 8(1), 1250. doi: 10.2903/j.efsa.2010.1250.Google Scholar
EFSA (2014). Explanatory statement for the applicability of the Guidance of the EFSA Scientific Committee on conducting repeated-dose 90-day oral toxicity study in rodents on whole food/feed for GMO risk assessment. EFSA Journal 12(10), 3871. doi: 10.2903/j.efsa.2014.3871.Google Scholar
EFSA (2015 a). Outcome of the Public Consultation on the Draft Guidance on the Agronomic and Phenotypic Characterization of Genetically Modified Plants. EFSA supporting publication 2015: EN-829. Parma, Italy: EFSA.Google Scholar
EFSA (2015 b). Guidance on the agronomic and phenotypic characterization of genetically modified plants. EFSA Journal 13(6), 4128. doi: 10.2903/j.efsa.2015.4128.Google Scholar
Endrenyi, L., Taback, N. & Tothfalusi, L. (2000). Properties of the estimated variance component for subject-by-formulation interaction in studies of individual bioequivalence. Statistics in Medicine 19, 2867–2878.3.0.CO;2-J>CrossRefGoogle ScholarPubMed
FDA (2014). Guidance for Industry: Bioavailability and Bioequivalence Studies Submitted in NDAs or INDs – General Considerations. Draft Guidance. Rockville, MD: Food and Drug Administration, Center for Drug Evaluation and Research (CDER). Available from: http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm389370.pdf (verified 3 December 2015).Google Scholar
Graybill, F. A. & Wang, C. M. (1980). Confidence intervals on nonnegative linear combinations of variances. Journal of the American Statistical Association 75, 869–873.CrossRefGoogle Scholar
Hannig, J., Iyer, H. & Patterson, P. (2006). Fiducial generalized confidence intervals. Journal of the American Statistical Association 101, 254–269.CrossRefGoogle Scholar
Harrigan, G. G., Culler, A. H., Culler, M., Breeze, M. L., Berman, K. H., Halls, S. C. & Harrison, J. M. (2013). Investigation of biochemical diversity in a soybean lineage representing 35 years of breeding. Journal of Agricultural and Food Chemistry 61, 10807–10815.Google Scholar
Harrison, J. M., Howard, D., Malven, M., Halls, S. C., Culler, A. H., Harrigan, G. G. & Wolfinger, R. D. (2013). Principle variance component analysis of crop composition data: a case study on herbicide-tolerant cotton. Journal of Agricultural and Food Chemistry 61, 6412–6422.Google Scholar
Herman, R. A., Scherer, P. N., Phillips, A. M., Storer, N. P. & Krieger, M. (2010). Safe composition levels of transgenic crops assessed via a clinical medicine model. Biotechnology Journal 5, 172–182.Google Scholar
Hothorn, L. A. & Oberdoerfer, R. (2006). Statistical analysis used in the nutritional assessment of novel food using the proof of safety. Regulatory Toxicology and Pharmacology 44, 125–135.Google Scholar
Howe, W. G. (1974). Approximate confidence limits on the mean of X + Y where X and Y are two tabled independent random variables. Journal of the American Statistical Association 69, 789–794.Google Scholar
Hyslop, T., Hsuan, F. & Holder, D. J. (2000). A small sample confidence interval approach to assess individual bioequivalence. Statistics in Medicine 19, 2885–2897.Google Scholar
John, J. A. & Mitchell, T. J. (1977). Optimal incomplete block designs. Journal of the Royal Statistical Society, Series B (Methodological) 39, 39–43.Google Scholar
Kang, Q. & Vahl, C. I. (2014). Statistical analysis in the safety evaluation of genetically modified crops: equivalence tests. Crop Science 54, 2183–2200.Google Scholar
Khuri, A. I., Mathew, T. & Sinha, B. K. (1998). Statistical Tests for Mixed Linear Models. New York: Wiley-Interscience.CrossRefGoogle Scholar
König, A., Cockburn, A., Crevel, R. W. R., Debruyne, E., Grafstroem, R., Hammerling, U., Kimber, I., Knudsen, I., Kuiper, H. A., Peijnenburg, A. A. C. M., Penninks, A. H., Poulsen, M., Schauzu, M. & Wal, J. M. (2004). Assessment of the safety of foods derived from genetically modified (GM) crops. Food and Chemical Toxicology 42, 1047–1088.Google Scholar
Krishnamoorthy, K. & Lian, X. D. (2012). Closed-form approximate tolerance intervals for some general linear models and comparison studies. Journal of Statistical Computation and Simulation 82, 547–563.Google Scholar
Krishnamoorthy, K. & Mathew, T. (2004). One-sided tolerance limits in balanced and unbalanced one-way random models based on generalized confidence intervals. Technometrics 46, 44–52.Google Scholar
Krishnamoorthy, K. & Mathew, T. (2009). Statistical Tolerance Regions: Theory, Applications, and Computation. Hoboken, NJ: Wiley.Google Scholar
Lee, Y. H., Shao, J. & Chow, S. C. (2004). Modified large-sample confidence intervals for linear combinations of variance components: extension, theory, and application. Journal of the American Statistical Association 99, 467–478.CrossRefGoogle Scholar
Liao, C. T., Lin, T. Y. & Iyer, H. K. (2005). One- and two-sided tolerance intervals for general balanced mixed models and unbalanced one-way random models. Technometrics 47, 323–335.Google Scholar
McNally, R. J., Iyer, H. & Mathew, T. (2003). Tests for individual and population bioequivalence based on generalized P values. Statistics in Medicine 22, 31–53.Google Scholar
OECD (1993). Safety Evaluation of Foods Derived by Modern Biotechnology: Concepts and Principles. Paris, France: Organization for Economic Cooperation and Development.Google Scholar
Park, D. J. & Burdick, R. K. (2003). Performance of confidence intervals in regression models with unbalanced one-fold nested error structures. Communication in Statistics – Simulation and Computation 32, 717–732.Google Scholar
Quiroz, J., Ting, N., Wei, G. C. G. & Burdick, R. K. (2002). Alternative confidence intervals for the assessment of bioequivalence in four-period cross-over designs. Statistics in Medicine 21, 1825–1847.Google Scholar
SAS Institute Inc. (2011). SAS/STAT® 9·3 User's Guide. Cary, NC: SAS Inst. Inc.Google Scholar
Schuirmann, D. J. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics 15, 657–680.Google Scholar
Searle, S. R. (1987). Linear Models for Unbalanced Data. New York: John Wiley and Sons.Google Scholar
Ting, N., Burdick, R. K., Graybill, F. A., Jeyaratnam, S. & Lu, T. F. C. (1990). Confidence interval on linear combinations of variance components that are unrestricted in sign. Journal of Statistical Computation and Simulation 35, 135–143.CrossRefGoogle Scholar
Tsui, K. W. & Weerahandi, S. (1989). Generalized P values in significance testing of hypotheses in the presence of nuisance parameters. Journal of the American Statistical Association 84, 602–607.Google Scholar
Vahl, C. I. & Kang, Q. (2015). Equivalence criteria for the safety evaluation of a genetically modified crop – a statistical perspective. The Journal of Agricultural Science, Cambridge. doi: S0021859615000271.Google Scholar
Van der Voet, H., Perry, J. N., Amzal, B. & Paoletti, C. (2011). A statistical assessment of differences and equivalences between genetically modified and reference plant varieties. BMC Biotechnology 11, 15. doi: 10.1186/1472–6750-11-15.CrossRefGoogle ScholarPubMed
Venkatesh, T. V., Breeze, M. L., Liu, K., Harrigan, G. G. & Culler, A. H. (2014). Compositional analysis of grain and forage from MON 87427, an inducible male sterile and tissue selective glyphosate-tolerant maize product for hybrid seed production. Journal of Agricultural and Food Chemistry 62, 1964–1973.Google Scholar
Ward, K. J., Nemeth, M. A., Brownie, C., Hong, B., Herman, R. A. & Oberdoerfer, R. (2012). Comments on the paper “A statistical assessment of differences and equivalences between genetically modified and reference plant varieties” by van der Voet et al. 2011. BMC Biotechnology 12, 13. doi: 10.1186/1472–6750-12-13.Google Scholar
Weerahandi, S. (1993). Generalized confidence intervals. Journal of the American Statistical Association 88, 899–905.Google Scholar
Whent, M., Hao, J. J., Slavin, M., Zhou, M., Song, J. Z., Kenworthy, W. & Yu, L. L. (2009). Effect of genotype, environment, and their interaction on chemical composition and antioxidant properties of low-linolenic soybeans grown in Maryland. Journal of Agricultural and Food Chemistry 57, 10163–10174.Google Scholar