ADVANTAGES AND DISADVANTAGES OF META-ANALYSIS

Pooling data from multiple studies increases the sample size and enhances the statistical power of the results and the accuracy of the calculated effect estimates. It is considered the most effective way to assess and examine the evidence for a specific issue, offering a high level of evidence and forming recommendations for clinical practice. However, the strength of the evidence provided depends closely on the quality of the studies included and the thoroughness of the meta-analytic process (Papakostidis & Giannoudis, 2023).

While meta-analysis has numerous advantages, it also has methodological weaknesses and potential difficulties interpreting overall results. This underscores the need for readers to maintain a critical approach, fostering a sense of responsibility and diligence.

The field of meta-analysis is not without its ongoing debates and limitations, which continue to attract attention. These include issues such as publication bias and omitted variable bias, which are essential to consider in the context of meta-analytic research.

Meta-analysis has many advantages over other research synthesis methods. Does this mean that meta-analysis is always preferred and that narrative reviews, combining probabilities, and vote-counting procedures must be abandoned altogether?

Among the various advantages, it is worth highlighting (Deeks et al., 2023; Koricheva et al., 2013):

• Meta-analysis provides a comprehensive literature assessment, offers a high level of evidence, and helps establish practice recommendations.
• Meta-analysis provides a more objective, informative, and powerful means of summarizing individual studies' results than narrative/qualitative reviews and vote counting.
• While the use of meta-analysis is on the rise, it is essential to note that understanding the method is valuable even if you are not planning to conduct your meta-analyses. This knowledge will enable researchers to follow and evaluate the literature in their field effectively.
• Applying meta-analysis to applied fields (e.g., conservation and environmental management) can make results more valuable for policymakers.
• Mastering the fundamentals of meta-analysis can significantly enhance the quality of data presentation in original research, making it possible to incorporate the findings into future research reviews.
• Conducting meta-analysis changes the way one reads and evaluates primary studies. It makes one acutely aware that the statistical significance of the results depends on statistical power and, in general, improves one's ability to evaluate evidence critically.
• To enhance precision: Many individual studies are too small to provide conclusive evidence about the effects of interventions. Precision is typically improved when estimates are based on a larger data pool.
• Primary studies typically target specific participants and well-defined interventions to address questions beyond the scope of individual studies. Combining studies with varying characteristics allows us to explore the consistency of effects across a broader range of populations and interventions. This approach can also help identify reasons for differences in effect estimates.
• Combining study results through statistical synthesis allows for a formal assessment of conflicting findings and exploration of reasons for varying results to resolve disputes from seemingly contradictory studies or to generate new hypotheses.

Meta-analysis alone or in combination with other research synthesis methods should be used whenever estimating the magnitude of an effect and understanding sources of variation in that effect is of interest and when at least some of the primary studies gathered provide sufficient data to carry out the analysis.

Emphasizing the importance of a critical approach, it becomes evident that it is crucial to identify deficiencies in methodology and interpret overall findings in meta-analyses. This approach addresses concerns about publication bias and the potential for erroneous findings when dissimilar studies with varying outcome data are included.

It is essential to note some of its drawbacks, such as excluding low-quality studies. As an alternative to meta-analysis, "best evidence synthesis" would only consider reputable studies. The challenge here is determining the criteria for distinguishing between good and bad. It is advisable to include as many papers as possible and give importance to various aspects of study design based on the widely approved methodological practice. This allows us to explore how different methods impact the estimated border effects. The impact factor of the publication vehicle and the number of citations each study receives must also be considered (Havranek & Irsova, 2016).

Replicability in research is of the utmost importance, as it enables other researchers to verify the findings and build upon the existing knowledge. To enable other researchers to reproduce our analysis, utilize the approach of seeking out studies that assess the impact of borders. It is acceptable to omit certain studies if their results do not systematically differ from those in our analysis.

Studies reporting numerous estimates significantly influence the meta-analysis. When each estimate is given equal weight, the imbalanced nature of data in meta-analysis means that studies with numerous estimates dictate the results. One potential solution is the mixed-effects multilevel model, which assigns approximately equal weight to each study if the estimates within the study are highly correlated. However, this method introduces random effects at the study level, which may be correlated with explanatory variables.

Authors' preferred estimates should carry more weight. Studies examining the border effect typically present numerous estimates and often favour a subset of these estimates (many results are presented as robustness checks). While some authors explicitly state their preferences, it is only possible to determine the preferred estimates for some studies. Instead, a researcher must control for data and methodology, which should be more straightforward to code and must encompass most of the authors' desires, such as controlling for multilateral resistance (Havranek & Irsova, 2016).

It is important to note that individual estimates are only partially independent due to authors utilizing similar data. When conducting meta-analysis, it is crucial to consider that individual clinical trials can be largely independent, particularly in medical research. However, most economic dataset's regression results and observations are not independent of economics. The dependence among observations is addressed by clustering the standard errors at the level of individual studies and datasets.

There are too many potential explanatory variables, and it needs to be clarified which ones should be included. With numerous aspects of study design, finding a theory that substantiates the inclusion of all of them is challenging. For instance, an option is to assign more weight to extensive studies published in reputable journals, but it needs to be evident why they should consistently report different results.

Meta-analysis compares dissimilar findings. In economics, meta-analysis examines heterogeneous estimates. Various estimates are produced using different methods, and it is necessary to account for differences in the design of primary studies. To enhance the comparability of the estimates in a dataset, choose only to include the results concerning the impact of specific common variables and exclude the extensive literature on the others.

Errors in data coding are inevitable. Compiling data for meta-analysis involves months of reading and coding the data. Do not use research assistants for this assignment because there is a risk of immediately moving to regression tables and coding the data without thoroughly reviewing the primary studies. However, it is impossible to eliminate errors; we can only minimize them by independently collecting, comparing, and correcting the datasets, ensuring the reliability of our research.

Publication bias undermines the validity of meta-analysis. Researchers may overestimate the mean reported effect size and not accurately represent the true effect size when they report estimates displaying a particular sign or statistical significance.

In conclusion, meta-analysis involves critical steps such as question definition, data collection, analysis, and reporting results. Defining the question is crucial in shaping the focus and direction of the research. While it offers high-level evidence and informs clinical practice, it also faces challenges related to methodological weaknesses, publication bias, and potential limitations in achieving its objectives. Despite these limitations, meta-analysis significantly contributes to evidence-based practice in healthcare by providing a comprehensive synthesis of available research.