# Response Surface Methodology Using Observational Data: A Systematic Literature Review
## 3 Methodology  
The systematic literature review conducted in the present study follows systematic literature review guidance from [36] and conforms to the PRISMA statement in [18]. We started by identifying studies and followed this with database searches, filtering processes, and content analyses, as shown in Figure 3.  
A systematic literature review gives an objective synthesis as it involves a decent number of references based on selected keywords. It follows the identification of studies, and the stages involve paper searches, filtering, and synthesis. As shown in Figure 3, identification steps define the problems in RSM, which are then formulated as research questions. By applying certain criteria based on the research questions, the collected pieces of literature were screened and analyzed with respect to descriptive, bibliometric, and comparative analysis. The Scopus database was deployed because it provided better article searching in terms of source titles, journal impact metrics, and the number of publishers when compared to others, as shown by [37]. The systematic literature review methodology was used to achieve a reproducible result in the development and application of RSM-OD. The analysis and discussion in this paper focused on those approaches accommodating nondesigned experimental data in the classic RSM. Moreover, as the context of this paper discusses the development of RSM-OD, the literature research questions (LRQs) emphasize how the standard RSM is modified to accept data. * LRQ1: What are the rationales for using observational data as an alternative to conducting a real RSM experiment? * LRQ2: What condition within observational data can be adapted to RSM? * LRQ3: How are observational data adopted to RSM? The descriptive analysis and synthesis stages in this paper attempted to answer those LRQs associated with the need for well-designed experimentally-based optimization in  
Figure 3: Systematic literature review framework based on PRISMA [18]. various fields of studies. The practical limitations of conducting the experiments were raised and prompted the consideration of adopting observational data as an alternative. As shown in Figure 3, the stages start with a bibliometric analysis to map the interrelationship among research keywords as a reference for methodological mapping and answering the LRQs. LRQ1 was answered by identifying the rationales for using observational data for RSM analysis, considering the limitations of classic RSM in practice but still referring to its standard procedures (Figure 1). As LRQ1 was answered, LRQ2 and LRQ3 can parallelly be processed. The answers to LRQ2 review strict assumptions of the RSM and how observational data can still be adopted by RSM. As a result, observational data preprocessing and evaluations to conform to the RSM analysis were identified. Meanwhile, LRQ3 dealt with the procedures to adopt observational data into the RSM analysis, subject to the classic statistical assumptions within, including DoE properties, modified mathematical models, and the optimization method. Finally, the discussion was started based on the results of LRQ1 to LRQ3, which focus on the opportunities and gaps for adopting observational data as an alternative to DoE in RSM analysis and open the potential development of further research. Search strings for paper abstracts and titles by restricting the search references from the Scopus database were predetermined to ensure that the papers still covered the proposed topics (Figure 4). Similar terms related to nonexperimental data used in the reference papers, such as observational, historical, or retrospective data, were found. Further, these terms were combined with common keywords in the RSM analysis, such as \"optimization\" and \"DoE\". Some Boolean operators were applied, considering that RSM-OD analysis should refer to the classic RSM stages. Figure 4 shows the search process. Based on the research questions, the key terms were \"RSM\", \"non-experimental data\", and \"optimization\".