Experimental design and statistical methods for classical and bioequivalence hypothesis testing with an application to dairy nutrition studies.

Genetically modified (GM) corn hybrids have been recently compared against their isogenic reference counterparts in order to establish proof of safety as feedstuffs for dairy cattle. Most such studies have been based on the classical hypothesis test, whereby the null hypothesis is that of equivalence. Because the null hypothesis cannot be accepted, bioequivalence-testing procedures in which the alternative hypothesis is specified to be the equivalence hypothesis are proposed for these trials. Given a Type I error rate of 5%, this procedure is simply based on determining whether the 90% confidence interval on the GM vs. reference hybrid mean difference falls between two limits defining equivalence. Classical and bioequivalence power of test are determined for 4 x 4 Latin squares and double-reversal designs, the latter of which are ideally suited to bioequivalence studies. Although sufficient power likely exists for classical hypothesis testing in recent GM vs. reference hybrid studies, the same may not be true for bioequivalence testing depending on the equivalence limits chosen. The utility of observed or retrospective power to provide indirect evidence of bioequivalence is also criticized. Design and analysis issues pertain to Latin square and crossover studies in dairy nutrition studies are further reviewed. It is recommended that future studies should place greater emphasis on the use of confidence intervals relative to P-values to unify inference in both classical and bioequivalence-testing frameworks.

[1]  M. Faust New feeds from genetically modified plants: the US approach to safety for animals and the food chain. , 2002 .

[2]  D. Heisey,et al.  The Abuse of Power , 2001 .

[3]  J. Jensen,et al.  Genetic evaluation of dairy cattle using test-day models. , 2001, Journal of dairy science.

[4]  D. Kleinschmit,et al.  Influence of glyphosate-tolerant (event nk603) and corn rootworm protected (event MON863) corn silage and grain on feed consumption and milk production in Holstein cattle. , 2003, Journal of dairy science.

[5]  T. R. Morris,et al.  Experimental Design and Analysis in Animal Sciences , 1999 .

[6]  H. L. Lucas Switchback Trials for More than Two Treatments , 1956 .

[7]  K. Phillips Power of the two one-sided tests procedure in bioequivalence , 1990, Journal of Pharmacokinetics and Biopharmaceutics.

[8]  J. L. Gill,et al.  Balanced Two-Period Changeover Designs for Several Treatments , 1976 .

[9]  L. Miles,et al.  Bioequivalence of coenzyme Q10 from over-the-counter supplements , 2002 .

[10]  The U.S. draft guidance regarding population and individual bioequivalence approaches: comments by a research-based pharmaceutical company. , 2000, Statistics in medicine.

[11]  J. L. Gill Evolution of Statistical Design and Analysis of Experiments , 1981 .

[12]  J. L. Gill Sample Size for Experiments on Milk Yield , 1969 .

[13]  H. L. Lucas Extra-Period Latin-Square Change-Over Designs , 1957 .

[14]  N R St-Pierre,et al.  Invited review: Integrating quantitative findings from multiple studies using mixed model methodology. , 2001, Journal of dairy science.

[15]  N R St-Pierre,et al.  Interpretation and design of nonregulatory on-farm feeding trials. , 1999, Journal of animal science.

[16]  J.B.M. Wilmink,et al.  Adjustment of test-day milk, fat and protein yield for age, season and stage of lactation , 1987 .

[17]  Donald J. Schuirmann A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability , 1987, Journal of Pharmacokinetics and Biopharmaceutics.

[18]  Meinhard Kieser,et al.  Proof of Safety in Toxicology Based on the Ratio of Two Means for Normally Distributed Data , 1999 .

[19]  A. Munk Connections between average and individual bioequivalence. , 2000, Statistics in medicine.

[20]  R. Berger,et al.  Bioequivalence trials, intersection-union tests and equivalence confidence sets , 1996 .

[21]  J. Folmer,et al.  Utilization of Bt corn residues by grazing beef steers and Bt corn silage and grain by growing beef cattle and lactating dairy cows. , 2002, Journal of animal science.

[22]  Walter W. Stroup,et al.  Power analysis based on spatial effects mixed models: A tool for comparing design and analysis strategies in the presence of spatial variability , 2002 .

[23]  G. Hartnell,et al.  Performance of lactating dairy cows fed corn as whole plant silage and grain produced from a glyphosate-tolerant hybrid (event NK603). , 2003, Journal of dairy science.

[24]  Margaret A. Nemeth,et al.  Applied Multivariate Methods for Data Analysis , 1998, Technometrics.

[25]  J. H. Clark,et al.  Livestock Performance: Feeding Biotech Crops , 2001 .

[26]  G. Hartnell,et al.  Effects of feeding silage and grain from glyphosate-tolerant or insect-protected corn hybrids on feed intake, ruminal digestion, and milk production in dairy cattle. , 2003, Journal of dairy science.

[27]  A. Haslberger,et al.  Substantial equivalence of antinutrients and inherent plant toxins in genetically modified novel foods. , 2000, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[28]  A. Freeny,et al.  Statistical Principles for Research Design and Analysis , 1994 .

[29]  R. Littell SAS System for Mixed Models , 1996 .

[30]  E. Williams Experimental Designs Balanced for the Estimation of Residual Effects of Treatments , 1949 .

[31]  R C Littell,et al.  Statistical analysis of repeated measures data using SAS procedures. , 1998, Journal of animal science.

[32]  J. L. Gill,et al.  Design and analysis of experiments in the animal and medical sciences , 1980 .