# Medical Application of Big Data: Between Systematic Review and Randomized Controlled Trials
## 6 Big Data Research Design
A: Due to the nature of observational research, it is not easy to unify the methodology of research, and there is a risk that results may vary depending on the methodology. Re-searchers can determine the optimal methodology through SR and avoid possible bias. Q: What should be kept in mind as a researcher when researching medical big data? A: Since methodology is important in observational studies, the accuracy of the definition of the disease, the accuracy of the definition of occurrence, and above all, the setting of the control group and index date are the most important. Q: How could we use SR in designing an observational study using medical big data specifically? A: In the research of a liver donor study using big data platforms, including Korea Centers for Disease Control and Prevention, Health Review and Assessment Service, National Statistical Office, Health Insurance claim data, SR was performed (Figure 3) [30]. Figure 3a revealed that both OR and HR showed no significant difference between living liver donors and health controls. However, there were inconsistent methodological designs which represented a high risk of bias. Hence, after performing scrupulous reviews for qualitative analysis using ROBIN1 using own-judgment criteria for this study (Table 2), qualitative analysis was completed (Figure 3b). By excluding items from studies with a high risk of bias and adopting items from studies with a low bias, a stable methodology for selection and exclusion of subjects and setting and matching of control groups could be designed (Figure 4) [31]. Figure 3: (**A**) quantitative analysis using meta-analysis; (**B**) qualitative analysis using ROBIN-I. “Adapted with permission from Ref. [30]. 2023, Springer Nature B.V.”. During quantitative analysis, almost all of the included studies carried a large population size and long period. These kinds of outcomes compared with controls are most appropriately analyzed using HRs and ORs. The random-effects model published by [PERSON] and [PERSON] [32] was used to determine the pooled overall incidence or mortality ratios with 95% confidence intervals for outcomes. Statistical heterogeneity was evaluated by the [PERSON]'s Q test and the I\({}^{2}\) statistic. Meta-regression analysis was conducted for each moderator [33]. Many researchers do not spend much time in the process of qualitative evaluation. Qualitative evaluation of RCTs [34] can be completed relatively easily, but qualitative evaluation of observational studies is not easy. A qualitative evaluation can only be performed properly when the criteria for the methodology for each item are established [35; 36]. We believe that an appropriate scientific methodology can only be created by following such a qualitative evaluation. Figure 3B evaluates how much the risk of bias is for the seven evaluation items. The problem here is that it is necessary to develop and evaluate indicators that can rank the risk for each of the seven evaluation items. The indicators developed in this way allow for detailed evaluation of these observational studies. Let us expand upon this. In the case of qualitative evaluation of items with bias due to confounding factors, we--who implement the SR--must separately create standards for this evaluation item. In this case, we did so based on whether or not it was matched and whether or not it was adjusted when calculating HR; if matching was completed and adjusted when calculating HR; if matching was completed, the risk of bias is not equal to the risk of bias. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \begin{tabular}{} \end{tabular}  \\  & (4) _Matched control, adjusted HR_ & & & & & & & \\  & (4) _Matched control, adjusted HR_ & & & & & & & \\  & (4) _Matched control, adjusted HR_ & & &  
HR, it was evaluated as low, while if only matching was not adjusted when calculating HR, it was evaluated as moderate.