Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-26T15:00:22.602Z Has data issue: false hasContentIssue false
  • English
  • Français

Meta-analyses of experimental data in animal nutrition⋆

Published online by Cambridge University Press:  01 August 2008

D. Sauvant ,
P. Schmidely ,
J. J. Daudin  and
N. R. St-Pierre
D. Sauvant*
Affiliation:
UMR Physiologie de la Nutrition et Alimentation, INRA-AgroParisTech, 16 rue Claude Bernard, 75231 Paris Cedex 05, France
P. Schmidely
Affiliation:
UMR Physiologie de la Nutrition et Alimentation, INRA-AgroParisTech, 16 rue Claude Bernard, 75231 Paris Cedex 05, France
J. J. Daudin
Affiliation:
ENGREF, Mathématique et Informatique Appliquée, INRA-AgroParisTech, 16 rue Claude Bernard, 75231 Paris Cedex 05, France
N. R. St-Pierre
Affiliation:
Department of Animal Sciences, The Ohio State University, 2029 Fyffe Rd., Columbus, OH-43210, USA
*
E-mail: [email protected]
Get access

Abstract

Research in animal sciences, especially nutrition, increasingly requires processing and modeling of databases. In certain areas of research, the number of publications and results per publications is increasing, thus periodically requiring quantitative summarizations of literature data. In such instances, statistical methods dealing with the analysis of summary (literature) data, known as meta-analyses, must be used. The implementation of a meta-analysis is done in several phases. The first phase concerns the definition of the study objectives and the identification of the criteria to be used in the selection of prior publications to be used in the construction of the database. Publications must be scrupulously evaluated before being entered into the database. During this phase, it is important to carefully encode each record with pertinent descriptive attributes (experiments, treatments, etc.) to serve as important reference points for the rest of the analysis. Databases from literature data are inherently unbalanced statistically, leading to considerable analytical and interpretation difficulties; missing data are frequent, and data structures are not the outcomes of a classical experimental system. An initial graphical examination of the data is recommended to enhance a global view as well as to identify specific relationships to be investigated. This phase is followed by a study of the meta-system made up of the database to be interpreted. These steps condition the definition of the applied statistical model. Variance decomposition must account for inter- and intrastudy sources; dependent and independent variables must be identified either as discrete (qualitative) or continuous (quantitative). Effects must be defined as either fixed or random. Often, observations must be weighed to account for differences in the precision of the reported means. Once model parameters are estimated, extensive analyses of residual variations must be performed. The roles of the different treatments and studies in the results obtained must be identified. Often, this requires returning to an earlier step in the process. Thus, meta-analyses have inherent heuristic qualities.

Keywords

meta-analysismixed modelsnutrition
Type
Full Paper
Information
animal , Volume 2 , Issue 8 , August 2008 , pp. 1203 - 1214
Copyright
Copyright © The Animal Consortium 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

⋆

*Salaries and research support were provided by state and federal funds appropriated to the Ohio Agricultural Research and Development Center, the Ohio State University.

References

Agresti, A 2002. Categorical data analysis, 2nd edition. John Wiley & Sons, Hoboken, New Jersey.Google Scholar
Bravo, D, Sauvant, D, Bogaert, C, Meschy, F III 2003. Quantitative aspects of phosphorus excretion in ruminants. Reproduction, Nutrition, Development 43, 285–300.Google Scholar
Cochran, WG 1968. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24, 295–313.Google Scholar
De Groot, MH 1970. Optimal statistical decision. Mc Graw-Hill, New York.Google Scholar
Draper, NR, Smith, H 1998. Applied regression analysis. John Wiley & Sons, Inc., New York.Google Scholar
Eugène, M, Archimède, H, Sauvant, D 2004. Quantitative meta-analysis on the effects of defaunation of the rumen on growth, intake and digestion in ruminants. Livestock Production Science 85, 81–91.Google Scholar
Firkins, JL, Eastridge, ML, St-Pierre, N, Noftsger, SM 2001. Invited: Effects of grain variability and processing on starch utilization by lactating dairy cattle. Journal of Animal Science 79 (E. Suppl.), E218–E238.Google Scholar
Glass, GV 1976. Primary, secondary and meta-analysis of research. Education Research 5, 3–8.Google Scholar
Grosman, M, Koops, WJ 1988. Multiphasic analysis of lactation curves in dairy cattle. Journal of Dairy Science 71, 1598–1608.Google Scholar
Lovatto, PA, Sauvant, D 2002. Méta-analyse et modélisation de l’ingestion volontaire chez le porc. Journées de la Recherche Porcine, Paris (FRA) 2002/02/05–07, 129–134.Google Scholar
Mantel, N, Haenszel, W 1959. Statistical aspects of the analysis of data from retrospective studies of disease. Journal National of Cancer Institute 22, 719–748.Google ScholarPubMed
Martin, O, Sauvant, D 2002. Meta-analysis of input/output kinetics in lactating dairy cows. Journal of Dairy Science 85, 3363–3381.CrossRefGoogle Scholar
McCulloch, CE, Searle, SR 2001. Generalized, linear, and mixed models. John Wiley & Sons, Inc., New York.Google Scholar
McPhee, MJ, Oltjen, JW, Famula, TR, Sainz, R 2006. Meta-analysis of factors affecting carcass characteristics of feedlot steers. Journal of Animal Science 84, 3143–3154.Google Scholar
Meschy, F, Bravo, D, Sauvant, D 2004. Analyse quantitative des réponses des vaches laitières à l’apport de substances tampon. INRA Productions Animales 17, 11–18.Google Scholar
Milliken GA 1999. Mixed models. Seminar to the Cleveland Chapter of the American Statistical Association, May 10.Google Scholar
Offner, A, Sauvant, D 2004. Prediction of in vivo starch digestion in cattle from in situ data. Animal Feed Science and Technology 111, 41–56.Google Scholar
Offner, A, Bach, A, Sauvant, D 2003. Quantitative review of in situ starch degradation in the rumen. Animal Feed Science and Technology 106, 81–93.Google Scholar
Rico-Gomez, M, Faverdin, P 2001. La nutrition protéique modifie l’ingestion des vaches laitières: analyse bibliographique. Rencontres Recherches Ruminants 8, 285–288.Google Scholar
Rubin, DB 1997. Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine 127, 757–763.Google Scholar
Sauvant, D 1992. La modélisation systémique en nutrition. Reproduction, Nutrition, Development 32, 217–230.Google Scholar
Sauvant, D 1994. Modelling homeostatic and homeorhetic regulations in lactating animals. Livestock Production Science 39, 105–113.Google Scholar
Sauvant, D, Martin, O 2004. Empirical modelling through meta-analysis vs mechanistic modelling. In Nutrient digestion and utilization in farm animals: modelling approaches (ed. E Kebreab). CAB International, London.Google Scholar
St-Pierre, NR 2001. Invited review: integrating quantitative findings from multiple studies using mixed model methodology. Journal of Dairy Science 84, 741–755.Google Scholar
St-Pierre, NR, Glamocic, D 2000. Estimating unit costs of nutrients from market prices of feedstuffs. Journal of Dairy Science 83, 1402–1411.Google Scholar
Schmidely, P, Glasser, F, Doreau, M, Sauvant, D 2008. Digestion of fatty acids in ruminants: a meta-analysis of flows and variation factors. 1. Total fatty acids. Animal 2, 677–690.Google Scholar
Tomassone, R, Lesquoy, E, Millier, C 1983. La régression: nouveau regard sur une ancienne méthode statistique. In Actualités scientifiques et agronomiques de l’INRA (ed. Masson). INRA, Paris.Google Scholar