## 8 Discussion and Future Perspective  
The current narrative review focuses on the rationale of federated facility, as well as the challenges and solutions developed to when attempting to maximize the advantages that are obtained from the federated facility of cohort studies. Assembling individual-level data can be a useful, particularly when the results of interest are relevant. There are several benefits of federated facility and harmonizing cohorts: integrating harmonized data allows for an increase in sample sizes, improves the generalizability of results [1; 7; 52], ensures the validity of comparative research, creates opportunities to compare different population groups by filling the gaps in the distribution (different age groups, nationality, ethnicity etc. ), facilitates extra proficient secondary use of data, and offers opportunities for collaborative and consortium research. Data pooling of different cohort studies faces many hurdles, including interoperability, shared access, and ethical issues, when cohorts working under different national regulations are integrated. It is essential that strong collaboration among different parties exists to effectively implement database federation and data harmonization [7]. A federated framework allows investigators to analyze data safely and remotely (i.e., produce summary statistics, contingency tables, logistic regressions)facilitating their accessibility and decreasing actual time restrictions without the burden of filing several data access requests at various research centers, thereby saving principal investigators and study managers time and resources [7]. An important aspect of our review is to provide insights into large samples that result from merging the datasets. Meta-analysis, or mega-analysis of studies, might lead to a more robust estimate of the magnitude of the associations ultimately increasingly the generalizability of findings [33]. As progressively thorough computations can be accomplished in a mega-analysis, some researchers reckon that mega-analysis of individual-participant data can be more efficient than meta-analysis of aggregated data [34]. The mega-analytical framework appears to be the more robust methodology due to the relatively high amount of variation detectable among cohorts in multi-center studies. In cohort studies, several methods are used to deal with missing data in the exposure and outcome analyses. The most common method is to perform a complete case analysis, an approach that might generate biased consequences if the missing data are not followed by the assumptions of missing completely at random (MCAR). The complete-case analysis allows for consistent results only when the missing data probabilities do not depend on the disease and exposure status simultaneously. Nowadays, researchers are using advanced statistical modeling procedures (for example, _MI_ and Bayesian) to handle missing data. Combining studies by Bayesian enable us to quantify the relative evidence with respect to multiple hypotheses using the information from multiple cohorts [44]. Missingness is a typical problem in cohort studies and it is likely to introduce substantial bias into the results. We highlighted how the unpredictable recording of missing data in cohort studies, if not dealt with properly, and the ongoing use of inappropriate approaches to handle missing data in the analysis can substantially affect the study findings leading to inaccurate estimates of associations [13]. Increasing the quality of the study design and phenotyping should be a priority to decrease the amount and impact of missing data. Robust and adequate study designs minimize the additional requests on participants and clinicians beyond routine clinical care, an aspect that encourages the implementation of pragmatic trial design [53]. An organization of databases can facilitate the use of innovative exploratory tools based on machine learning and data mining for data analyses due to data-harmonization techniques.