Meta-analyses of experimental data in the animal sciences

In certain areas of animal research, such as nutrition, quantitative summarizations of literature data are periodically needed. In such instances, statistical methods dealing with the analysis of summary data (generally from the literature) must be used. These methods are known as meta-analyses. The implementation of a meta-analysis is done in several phases. The first phase defines the study objectives and identifies the criteria for selecting prior publications to be used in the construction of the database. Publications must be scrupulously evaluated for their quality 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 later on. Statistically, databases from literature data are inherently unbalanced, leading to considerable analytical and interpretation difficulties. Missing data are frequent, and data are not the outcomes of a classical experimental system. A graphical examination of the data is useful in getting a global view of the system as well as to hypothesize specific relationships to be investigated. This phase is followed by a statistical study of the meta-system using the database previously assembled. The statistical model used must follow the data structure. Variance decomposition must account for inter-and intra-study 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 that can guide in the design of future experiments as well as aggregating prior knowledge into a quantitative prediction system.