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

Abstract

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.

Meta-analysis of the effects of laidlomycin propionate, fed alone or in combination with chlortetracycline, compared with monensin sodium, fed alone or in combination with tylosin, on growth performance, health, and carcass outcomes in finishing steers in North America1

“…To avoid this potential bias, data not available to the public domain (e.g., industry reports) as well as unindexed reports (e.g., conference proceedings) were also included in this meta-analysis. In addition to articles indexed in electronic databases, a hand search of references in review papers as well as communication with scientists, manufacturing companies, and industry partners, who have been working in the relevant field, are all considered important methods for a comprehensive search (Lean et al, 2009). It may Figure 3.…”

“…not be possible to find every relevant study on a topic, but pursuing multiple avenues to find as many relevant studies as possible can minimize publication bias and increase the precision of the pooled estimates of effect while providing meaningful results of most importance to decision makers (Lean et al, 2009).…”

“…Mean and SEM reported using the English system (lb) were transformed to the International System of Units system (kg) by multiplying those values by 0.453592. When an overall SEM was reported for each treatment group, a pooled SD was derived from the formula SD p = SEM p × (n p ) 1/2 , in which SD p is the pooled SD, SEM p is the overall SEM provided by the study, and n p is the number of individuals in both treatment groups (Lean et al, 2009). For studies reporting only P-values, an estimate of a common SD was computed using the mean treatment difference (x 2 -x 1 ), the t-statistic (t αdfE which corresponds to the percentile from the reference distribution [tinv is the two-tailed inverse of the Student's t-distribution which can be calculated using (P-value, df); df = (n LP + n MS) -2)]), and the sample size of each treatment group (n) using the following formula reported in Mederos et al (2012)…”

Meta-analysis of the impact of stocking rate on the productivity of pasture-based milk production systems

“…However, results from a single experiment will not provide a definitive understanding of the effect of SR treatment because the conditions under which observations are made in a single experiment are inevitably narrow (Sauvant et al, 2008). A meta-analysis approach (Glass, 1976), summarising the results across published studies in a particular area and in combination with new statistical techniques, allows increased precision of analysis of effects across multiple experiments (St-Pierre, 2001;Sauvant et al, 2008;Lean et al, 2009). In a historical review of SR experiments, Journet and Demarquilly (1979) reported that an SR increase of one cow/ha resulted in a mean reduction in milk production per cow of 10%, but an increase in production per ha of over 20%.…”

The staircase of evidence – a new metaphor displaying the core principles of Evidence-based Veterinary Medicine

“…Metaanalyses summarize and combine information, weight and analyse the results of independent studies relating to a specific topic. Hence by increasing the sample size and statistical power the precision of an estimated outcome can be increased (Lean et al, 2009 (Gugiu et al, 2010). This appraisal should address aspects like enrolment criteria, the allocation in experimental designs, blinding, the sample size, statistical methodologies and the objectiveness in the discussion of the results.…”