Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 10, 2022
Date Accepted: Jan 3, 2023
Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation
ABSTRACT
Background:
When conducting observational studies using real-world data, it is essential to understand the content of the dataset and clarify the research questions. However, accurate screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research using real-world data have not yet been established.
Objective:
We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastases for data attainability screening at the preliminary step of observational studies using real-world data.
Methods:
The PAR framework was proposed to assess the data attainability from a particular data source during the early research process. We used the Samsung Medical Center breast cancer registry, a hospital-based real-time registry, constructed by leveraging the institution's de-identified and anonymized clinical data warehouse platform (N=45,219, from 1995 to 2021). Seven breast cancer experts in the Republic of Korea participated in a survey from September to October 2021. An interdisciplinary working group participated in evaluating the PAR framework. The PAR framework, starting with the clarification of the clinical question, contains four sequential stages: 1) operational definitions of variables, 2) data matching (structure/semantic), 3) data screening and extraction, and 4) data attainability diagram. Five clinical questions were identified through interviews with breast cancer experts to evaluate the PAR framework. The primary outcome was the screening results of clinical questions by preliminary attainability assessment for a real-world data resource. Secondary outcomes included a procedural diagram of data attainability for each research question.
Results:
Data attainability was tested for study feasibility according to the PAR framework with five clinical questions. We obtained datasets that were sufficient to conduct studies with four questions. Among these, only one question could be answered using direct data variables. The other three questions required surrogate definitions that combined data variables. The final question was not suitable for conducting this research.
Conclusions:
The PAR framework can improve real-world data research efficiency by accessing data attainability during the preliminary stage. Clinical Trial: N/A
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