Why perform meta analysis

Meta-analyses are used in grant applications to justify the need for a new study. The meta-analysis serves to put the available data in context and to show the potential utility of the planned study. The graphical elements of the meta-analysis, such as the forest plot, provide a mechanism for presenting the data clearly, and for capturing the attention of the reviewers.

Some funding agencies now require a meta-analysis of existing research as part of the grant application to fund new research. Comprehensive Meta-Analysis is a powerful computer program for meta-analysis. The program combines ease of use with a wide array of computational options and sophisticated graphics. Complex analyses can be conducted easily using this intuitive software.

The support staff are always helpful and respond quickly when questions arise. I highly recommend CMA. Erika A. Email Support Meta-Analysis.

View our support packages here. Comprehensive Meta-Analysis. Resources for Meta-Analysis. Why perform a meta-analysis? However, meta-analytic data from several individual studies may provide a clearer picture of the subgroup.

Meta-analysis increases statistical power by increasing the sample size, and can determine small but clinically significant effects by combining data from numerous studies. Statistically combining data from individual studies can provide a more precise estimate of the underlying effects than a single study. Thus, meta-analysis overcomes the limitation of small sample sizes of individual studies, detects the effects of interest, and reduces the risk of false-negative results.

Moreover, meta-analysis can settle controversies resulting from studies with conflicting results. In addition, combining primary studies with varying sample sizes and patient populations can increase the generalizability of the results of individual studies; this allows the results of the meta-analysis to be generalized to a wider population. Appropriately examining the heterogeneity between individual studies allows the testing of novel hypotheses that have not been proposed in previous studies [ 17 ].

As meta-analysis summarizes currently existing knowledge, it may help in identifying areas that lack adequate evidence, thereby producing new research questions. Meta-analysis overcomes the problems and biases of traditional narrative reviews through a more transparent and subjective process that includes a systematic methodological approach. Weaknesses of meta-analysis The methodological weaknesses of meta-analysis are listed in Table 2. In addition, the limitations of meta-analysis as well as suggestions for addressing these have been described [ 8 , 13 , 14 , 18 ].

One number cannot summarize a research field Summarizing large amounts of varying information using a single number is a controversial aspect of meta-analysis [ 19 ], as it ignores the fact that treatment effects may vary from study to study. However, a meta-analysis generalizes results despite differences in primary research and does not simply report a summary effect.

If there is substantial heterogeneity, then the focus should shift from the summary effect to the heterogeneity itself. Meta-analysis provides a variety of tools to assess the pattern of heterogeneity, and to possibly explain it. Meta-analysis should be avoided if studies are too heterogeneous to be comparable, as the metaanalytical results may be meaningless and true effects may be obscured.

However, meta-analyses, by their very nature, address broader questions than individual studies. Therefore, it can be said that a meta-analysis is similar to asking a question about fruits, for which both apples and oranges can contribute valuable information. Meta-analysis includes a set of criteria for determining which studies to analyze.

Hence, meta-analysis should be based on stricter criteria regarding the quality of studies to be included. When the available studies are flawed, a meta-analysis may employ sensitivity analyses to identify the influence of study biases.

Heterogeneity In meta-analysis, heterogeneity refers to the degree of dissimilarity in the results of individual studies [ 2 ]. The main assumption for performing meta-analysis is that studies are homogenous in terms of populations, interventions, controls, and outcomes. Assessing the heterogeneity between primary studies is an important step in conducting a meta-analysis [ 21 ].

If there is substantial heterogeneity, the focus of the analysis should be on exploring and understanding the sources of the variation. Meta-analysis examines the existence of heterogeneity among primary studies and analyzes the variance in their results [ 2 ]. Subgroup analyses and meta-regression are used to explore the sources of heterogeneity.

However, if there is a considerable amount of heterogeneity, it may not be appropriate to pool data in a meta-analysis. Publication bias Studies that report positive effects tend to be published more frequently than those that do not, and studies that report no significant results usually remain unpublished [ 22 ]. As meta-analysis includes only published studies, it may overestimate the actual magnitude of an effect [ 22 ].

Not all variables are comparable Some variables have no comparable measure for meta-analysis. Therefore, it may sometimes be necessary to construct new variables that present comparable concepts or restrict the analyses to common elements. Meta-analysis can disagree with randomized trials The main reason for discrepancies in meta-analysis is that it is based on heterogeneous and often small studies.

The subjects in the individual studies may substantially differ with respect to diagnostic criteria, comorbidities, severity of disease, and geographic region. In contrast, in large randomized controlled trials, the target population is more limited. However, meta-analysis that is conducted appropriately may provide complementary valuable information.

Meta-analysis cannot overcome subjectivity Meta-analysis relies on shared subjectivity, rather than objectivity. There is often a certain amount of subjectivity when deciding how similar studies should be before it is appropriate to combine them. Every form of analysis, including narrative reviews, requires certain subjective decisions.

However, such decisions are always explicitly stated in a meta-analysis. Meta-analysis deals only with the main effects Meta-analysis deals with the main effects, and its results can be generalized to the target population. However, the effects of interactions may also be examined by moderator analysis. Moreover, meta-analysis provides a more objective appraisal of the evidence than narrative review, and attempts to minimize bias by utilizing a methodological approach.

Meta-analysis provides a more precise estimate of the effect size and increases the generalizability of the results of individual studies. Therefore, it may enable the resolution of conflicts between studies, and yield conclusive results when individual studies are inconclusive. However, there are many caveats in the application of meta-analysis. Conclusions derived from meta-analysis are susceptible to the methodological quality of included studies, as well as to publication bias and the formulation of eligibility criteria.

Although combining the data from independent studies using meta-analytical methods can improve statistical precision, it cannot altogether prevent bias. However, many criticisms of meta-analysis are true for narrative reviews as well [ 22 ].

Although meta-analysis is criticized for its limitations, solutions to these problems exist. A systematic approach and transparency in conducting meta-analysis help to resolve conflicts and uncertainties between studies and to derive meaningful conclusions.

The use and value of metaanalysis is likely to increase in the future based on its power to reveal new findings. Clinical Neurology and Neurosurgery, , Nakamura, A. Physical activity during pregnancy and postpartum depression: Systematic review and meta-analysis. Journal of Affective Disorders, , A phenomenon in which studies with positive results have a better chance of being published, are published earlier, and are published in journals with higher impact factors.

Therefore, conclusions based exclusively on published studies can be misleading. A Meta-Analysis pools together the sample populations from different studies, such as Randomized Controlled Trials, into one statistical analysis and treats them as one large sample population with one conclusion.