To enhance international and joint research collaborations in prostate cancer research, data from different sources should use a common data model (CDM) that enables researchers to share their analysis scripts and merge results. The OMOP CDM maintained by OHDSI is such a data model developed for a federated data analysis with partners from different institutions that want to jointly investigate research questions using clinical care data. The German Cancer Society as the scientific lead of the Prostate Cancer Outcomes (PCO) study gathers data from prostate cancer care including routine oncological care data and survey data (incl. patient-reported outcomes) and uses a common data specification (called OncoBox Research Prostate) for this purpose. To further enhance research collaborations outside the PCO study, the purpose of this article is to describe the process of transferring the PCO study data to the internationally well-established OMOP CDM. This process was carried out together with an IT company that specialised in supporting research institutions to transfer their data to OMOP CDM. Of n = 49,692 prostate cancer cases with 318 data fields each, n = 392 had to be excluded during the OMOPing process, and n = 247 of the data fields could be mapped to OMOP CDM. The resulting PostgreSQL database with OMOPed PCO study data is now ready to use within larger research collaborations such as the EU-funded EHDEN and OPTIMA consortium.
为加强前列腺癌研究领域的国际合作与联合研究，不同来源的数据应采用通用数据模型（CDM），使研究人员能够共享分析脚本并整合研究结果。由OHDSI维护的OMOP CDM正是为此开发的数据模型，支持多机构合作伙伴利用临床诊疗数据进行联合研究分析。作为前列腺癌结局研究（PCO）的学术牵头方，德国癌症协会收集了包含常规肿瘤诊疗数据和调查数据（含患者报告结局）的前列腺癌诊疗数据，并为此制定了统一数据规范（称为OncoBox Research Prostate）。为进一步拓展PCO研究之外的科研合作，本文旨在阐述将PCO研究数据转换为国际通用的OMOP CDM的实施流程。该转换过程由专业支持研究机构数据OMOP化转型的IT公司协同完成。在涉及49,692例前列腺癌病例（每例含318个数据字段）的数据集中，OMOP化过程中排除了392例病例，其中247个数据字段成功映射至OMOP CDM。最终构建的PostgreSQL数据库现已整合OMOP化PCO研究数据，可支持欧盟资助的EHDEN和OPTIMA联盟等大型研究合作项目。
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