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Invited review: Use of meta-analysis in animal health and reproduction: Methods and applications

Lean, I.J., Rabiee, A.R., Duffield, T.F., Dohoo, I.R.
Journal of dairy science 2009 v.92 no.8 pp. 3545-3565
livestock, animals, animal health, animal reproduction, meta-analysis, methodology, epidemiology, estimation, risk factors, statistical analysis, quantitative analysis, Bayesian theory, data analysis, animal well-being
The objectives of this paper are to provide an introduction to meta-analysis and systematic review and to discuss the rationale for this type of research and other general considerations. We highlight methods used to produce a rigorous meta-analysis and discuss some aspects of interpretation of meta-analysis drawing on examples from the animal and veterinary science literature. Meta-analysis is a rapidly expanding area of research that has been relatively underutilized in animal and veterinary science. It is a quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes, than any individual study contributing to the pooled analysis. The examination of variability or heterogeneity in study results is also a critical outcome. The benefits of meta-analysis include a consolidated and quantitative review of a large, and often complex, sometimes apparently conflicting, body of literature. Meta-analytic methods place less emphasis on dichotomous outcomes from null hypothesis significance testing and greater emphasis on determining the magnitude and the precision of an effect of interest. A substantial benefit of meta-analysis is the potential to investigate new hypotheses using existing data, both through the development of a priori hypotheses and by examination of the heterogeneity in study responses. The specification of the outcome and hypotheses that are tested is critical to the conduct of meta-analyses, as is a sensitive literature search. A failure to identify the majority of existing studies can lead to erroneous conclusions; however, there are methods of examining data to identify the potential for studies to be missing; for example, by the use of funnel plots. Many of the statistical methods to conduct meta-analysis are widely used. Bayesian methods are well suited to meta-analysis. The post-hoc methods used to evaluate heterogeneity and publication bias, which include the I ² statistic, L'Abbé plots, Galbraith plots, Rosenthal's N, and influential study analysis are exclusively used in meta-analysis. Examples where meta-analyses have been repeated in animal science or veterinary medicine show good consistency in estimates of effect. Findings of studies to date have provided new understandings of rumen modifiers, milk fever, parasite control, mastitis, somatotropin, and reproductive manipulations. Rigorously conducted meta-analyses are useful tools to improve animal well-being and productivity. The need to integrate findings from many studies ensures that meta-analytic research is desirable and the large body of research now generated makes the conduct of this research feasible.