Significance of fragility index in meta-analysis

To the Editor, Meta-analysis is a statistical method where the researcher combines different but similar studies to estimate the overall pooled effect of an intervention. It is a powerful tool for synthesizing evidence. Systematic reviews (with homogeneity) of randomized controlled trials (RCTs) are considered as a 1a level of evidence as per the center of evidence-based medicine. It also helps in making recommendations about clinical practice and policy-making as well. The interpretation of meta-analysis is not only based on its ability to combine studies but also on other factors like quality of study, number of included studies, heterogeneity, etc. , However, meta-analyses can be fragile, meaning that their results are susceptible to slight changes in the data. The fragility index (FI) is one statistical tool used to assess how robust a meta-analysis's conclusions are. FI was earlier designed for RCT by Walsh et al. and later applied to the meta-analysis by Atal I et al. 1, 2 FI for RCT is defined as the minimum number of patients whose status needs to change from an "event" to a "nonevent" so as to change the result from statistically significant to nonsignificant and vice versa. 2 FI for meta-analysis is the minimum number of patients from one or more trials that must be included in the meta-analysis in order for a change in the event status (such as turning events into nonevents or nonevents into events) to have a significant impact on the statistical significance of the pooled effect or vice versa. 3 The larger the FI, the more robust the meta-analysis's data. An FI of 10 or more indicates that the results are very robust and, the results of the meta-analysis are less likely to be changed by small changes in the data. FI of 5 or less indicates that the results are fragile and the results of the meta-analysis are likely to be modified by small changes in the data. The FI can also be used to identify potential sources of bias in a meta-analysis. The FI is a valuable tool for assessing the robustness of the results of a meta-analysis. However, it is essential to note that the FI is not a perfect measure of quality. Other factors, such as the methodological quality of the studies included in the meta-analysis, can also affect the robustness of the results. There are some of the limitations of the FI. It is only applicable to meta-analyses that use dichotomous outcomes. FI can be difficult to interpret in meta-analyses with a small number of studies. The FI is calculated based on the number of events. This means that a meta-analysis with a small number of participants but a high number of events could still have a high FI. In addition to the FI, there are other methods that like trial sequential analysis also can be used to assess the robustness of the results of a meta-analysis. 4 These methods can be used in conjunction with the FI to get a more comprehensive assessment of the quality of a meta-analysis. The FI is a relatively new tool, and it is still being used and developed. However, it has the potential to be a valuable tool for assessing the robustness of the results of meta-analyses. By using the FI, the researchers can make more informed decisions about the reliability of the evidence and its implications for clinical practice. There are a number of factors that can contribute to a high FI. These include: Small sample sizes: Meta-analyses with small sample sizes are more likely to be fragile than meta-analyses with large sample sizes. This is because small sample sizes are less likely to be representative of the true population effect. Heterogeneity: Meta-analyses with heterogeneous results are more likely to be fragile than meta-analyses with homogeneous results. Heterogeneity refers to the variability of the results across the included studies. Bias: Meta-analyses with studies that are biased toward a particular outcome are more likely to be fragile than meta-analyses with studies that are unbiased. FI for meta-analysis is calculated with the help of a web interface available at http: //clinicalepidemio. fr/fragilityₘa/. 5 To conclude, although FI has emerged as a powerful tool to evaluate the robustness of study findings of meta-analyses, its application within the context of meta-analysis presents a unique challenge that warrants careful consideration as it has its limitations along with its strengths. FI can also be added as a tool for critical appraisal of the meta-analysis. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.