The Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) can be used for new drug application studies as well as secondarily for creating a clinical research data warehouse to leverage clinical research study data across studies conducted within the same disease area. However, currently not all clinical research uses Clinical Data Acquisition Standards Harmonization (CDASH) beginning in the set-up phase of the study. Once already initiated, clinical studies that have not utilized CDASH are difficult to map in the SDTM format. In addition, most electronic data capture (EDC) systems are not equipped to export data in SDTM format; therefore, in many cases, statistical software is used to generate SDTM datasets from accumulated clinical data. In order to facilitate efficient secondary use of accumulated clinical research data using SDTM, it is necessary to develop a new tool to enable mapping of information for SDTM, even during or after the clinical research. REDCap is an EDC system developed by Vanderbilt University and is used globally by over 2100 institutions across 108 countries. In this study, we developed a simulated clinical trial to evaluate a tool called REDCap2SDTM that maps information in the Field Annotation of REDCap to SDTM and executes data conversion, including when data must be pivoted to accommodate the SDTM format, dynamically, by parsing the mapping information using R. We confirmed that generating SDTM data and the define.xml file from REDCap using REDCap2SDTM was possible. Conventionally, generation of SDTM data and the define.xml file from EDC systems requires the creation of individual programs for each clinical study. However, our proposed method can be used to generate this data and file dynamically without programming because it only involves entering the mapping information into the Field Annotation, and additional data into specific files. Our proposed method is adaptable not only to new drug application studies but also to all types of research, including observational and public health studies. Our method is also adaptable to clinical data collected with CDASH at the beginning of a study in non-standard format. We believe that this tool will reduce the workload of new drug application studies and will support data sharing and reuse of clinical research data in academia.