Original research
They are useful data sources for answering a wide spectrum of clinical questions, ranging from disease burdens to prognoses, but are lacking regular follow-up visits. 7 In order to enhance the use of real-world data, several guid- ance documents are readily available that discuss the key issues about data sources for pharmacoepidemiology studies. 15–17 OBSERVATIONAL STUDIES USING ROUTINELY COLLECTED DATA Observational studies are the most common approach to using routinely collected data. A common research flow may be used when planning and implementing such studies (figure 1). RESEARCH QUESTION, STUDY PLANNING AND DESIGN In observational studies using RCD, the initial step is to specify a clear and answerable research question that contains the key components, including population, exposure, comparator (if applicable), outcome and timing. A multidisciplinary team would usually be developed which is responsible for the plan- ning, design, and implementation of a study. In the study plan- ning, the research team needs to identify potential data sources and determine the appropriateness of the data. The data appro- priateness often varies by research questions. However, it may commonly be assessed in dimensions including representative- ness, size of data, availability, completeness and accuracy of key research variables, and duration of database coverage. 18 In observational studies using RCD, study designs may be highly variable and are typically retrospective in nature. Retrospective cohort studies, case–control studies or nested
healthcare without a priori research purposes, their quality and applicability are often issues of methodological concerns. 7 8 The quality of RCD may be assessed in two dimensions— completeness and accuracy. 9 Completeness refers to the extent to which data are missing from the research perspective. For example, while information regarding cigarette smoking is important for many epidemiological studies, this information may often go unrecorded in routine practice. 10 Missing data are inevitable in RCD. However, understanding the extent to which important variables are missing among RCD and poten- tial reasons for them missing is often needed. Another important dimension is accuracy. Information in electronic medical records, such as disease codes or numerical values, may some- times be recorded inaccurately. Also, the underlying reasons may vary. 11 Validation of data is often needed when applying RCD for research purposes, and the involvement of manual checking is also often needed. 12 One should also assess the relevance of data. In the generation of RWE, the choice of data should always be made according to predefined research purposes. 13 For example, claims data may be more suitable for studies on health economics and treat- ment patterns; however, they may not provide sufficient infor- mation on patient characteristics, laboratory results or clinical endpoints, which are crucial for studies assessing treatment effects. 14 In another example, spontaneous adverse events report databases may often be used for detecting a signal of adverse events or generating hypotheses, but are of limited relevance for testing a hypothesis about adverse drug reaction. In the third example, electronic health records contain abundant clinical information, such as operation, imaging and laboratory results.
Figure 1 Schematic flow of observational study using routinely collected data.
Liu M, et al . Eur J Hosp Pharm 2022; 29 :8–11. doi:10.1136/ejhpharm-2021-003081
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