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Primary data and secondary data are two types of data used in research. The main difference between the two is that primary data is collected directly from the source, while secondary data is collected from sources that have already collected the data (i.e., secondary use of existing data).
Primary data is original data that is collected for a specific research project. This type of data can be collected through various methods, including surveys, interviews, observations, and experiments. Primary data is collected with a specific research objective in mind, and the data is usually more focused and targeted than secondary data.
On the other hand, secondary data is data that has already been collected by someone else for a different purpose. This type of data can be collected from a wide variety of sources, including healthcare organisations, government agencies, academic institutions, and commercial organizations. Secondary data can be used to supplement primary data or to answer research questions that are not directly related to the original research objective.
There are advantages and disadvantages to both types of data. Primary data is more likely to be accurate and relevant to the specific research question being studied, but it can also be more time-consuming and expensive to collect. Secondary data is generally less expensive and easier to access, but it may not be as accurate or relevant to the specific research question being studied.
In general, researchers will use a combination of primary and secondary data to address their research questions and achieve their research objectives.
Secondary Use of Existing Data
Secondary use of existing data refers to the practice of analyzing data that was collected for a different purpose than the current research question. This approach is becoming increasingly popular in real-world research because of the large amounts of data that are available through various sources, such as electronic health records, administrative databases, and social media.
In many cases, secondary data analysis can provide valuable insights and answer research questions that would otherwise be difficult or impossible to answer with primary data collection. For example, researchers can use existing data to study disease trends, evaluate the effectiveness of health interventions, and identify risk factors for various health outcomes.
A current, well published example is DARWIN EU®, the Data Analysis and Real-World Interrogation Network, which recently celebrated its first year of establishment. The platform aims to generate real-world evidence (RWE) to support the decision-making of EMA scientific committees and national competent authorities [Link] [1].
DARWIN EU® has initiated its first four studies using real-world data (RWD) from across Europe to better understand diseases, populations and the uses and effects of medicines. These first four studies start to demonstrate the benefits of DARWIN EU®. The use of a common data model, standardised analytics and agile processes allow faster performance of studies, increased capacity, and lower costs. The design and conduct of these first studies have also supported the establishment of analytical pipelines and processes. The studies were not linked to individual medicines currently under evaluation procedures but selected based on previous procedures and requests for RWE from EMA committees [1].
According to recent DARWIN EU® news [1], the use of a common data model, standardised analytics and agile processes allow faster performance of studies, increased capacity, and lower costs. Additionally, secondary data analysis can allow researchers to study topics that may not have been feasible to study with primary data collection due to ethical or practical limitations.
Some of the challenges associated with secondary use of existing data in the context of electronic health data in the EU, relate to determining the regulatory requirements for data access in the country of interest e.g., GDPR compliance + health research regulation compliance. We’ll explore this more later in the year and provide you examples and use cases.
There are also potential limitations to secondary data analysis, such as the lack of control over the quality and accuracy of the data, and the potential for biases and confounding factors that were not accounted for in the original data collection. Therefore, researchers must carefully evaluate the suitability of existing data for their research question and take steps to address any limitations or potential biases in the data.
References
1. European Medicines Agency – DARWIN EU® has completed its first studies and is calling for new data partners (28 March 2023)
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