## 5 Meta-analysis and mega-analysis  
Researchers are currently analyzing large datasets to clarify the biological underpinnings of diseases that, particularly in complex disorders, remain obscure. However, due to privacy concerns and legal complexities, data hosted in different centers cannot always be directly shared. In practice, data sharing is also hindered by the administrative burden that is associated with the need to transfer huge volumes of data. This situation made researchers to look for an analytical solution within meta-analysis or federated learning paradigms. In the federated setting, a model is fitted without sharing individual data across centers, but only using model parameters. Meta-analysis instead performs statistical testing by combining results from several independent analyses, for instance, by sharing p-values, effect sizes, and/or standard errors across centers [32].  
[PERSON] and co-authors have recently proposed two additions to the splitting approach for meta-analysis: splitting in a cohort and splitting cohorts [9]. The first method implies that data for each cohort is divided, monitored by a choice of variables on one subset and calculation of p-values on the other subset, and by meta-analysis across all cohorts [9]. This is a typical addition of the data splitting approach that can be applied to numerous cohorts. The second method comprises splitting cohorts as an alternative. Cohorts are divided into two groups, one group is used for variable selection and the other is used for attaining p-values as well as meta-analysis. This is a more applicable method, since it simplifies the analysis burden for each study and decreases the possibility of errors [9].  
As the focus of a meta-analysis is on the creation of summary statistics obtained from several studies, this method is most efficient when the original individual records used in prior analyses are not accessible or no longer collected [22]. The individual-level information can be pooled into a single harmonized dataset upon which mega-analyses are carried out [33]. The increased flexibility in handling confounders at the individual patient level and assessing the impact of missing data are substantial benefits of a mega-analytical method [34]. Mega-analyses have also been endorsed to evade the assumptions of within-study normality and recognize the within-study variances, which are particularly challenging with small samples. In spite of these benefits, mega-analysis requires homogeneous data sets and the creation of a shared centralized database [34].  
Meta-analysis has several disadvantages, including the presence of high level of heterogeneity [35], unmeasured confounders [36], limitation by ecological fallacy [37]. In addition, most of the primary studies included in meta-analysis are conducted in developed or western countries [38]. However, there are numerous benefits to directly fitting models straight into the original raw data instead of creating the applicable summary statistics. Current technological developments (such as a superior capacity for data sharing and wide opportunities for electronic data storage and retrieval) have increased the feasibility of retrieving original individual records for secondary analysis. This gives new opportunities for the progress of different approaches to integrate results across studies by using original individual records to overcome some of the inevitable limitations of meta-analysis [22].  
Here, we focus on approaches of integrative data analysis within the psychological sciences, as approaches to collecting current data can differ across disciplines.