What Are the Best Practices for Data Matching in Epidemiology?
To ensure successful data matching, the following best practices should be considered:
Data standardization: Ensure that data from different sources follow standardized formats and terminologies. Data cleaning: Address data quality issues such as missing values, duplicates, and inconsistencies before attempting to match records. Use of appropriate techniques: Choose the matching technique that best fits the data and the research question. Validation: Validate the matching results through manual review or by comparing with known benchmarks. Privacy protection: Implement measures to protect the confidentiality and privacy of the data being matched.
