Prospective data collection gives researchers control over the quality of their data. While poor statistical analyses can be run again, ".a badly designed study with inferior data is beyond the redemption of the most sophisticated statistical technique". An ongoing process with data entered and checked as they become available allows timely recovery of errors and missing data.ĭata that are highly reliable and complete are essential to unbiased, high-quality research studies. Conclusionĭata management for prospective studies should be planned well in advance of data collection. The amount of time required for data management was substantially underestimated. Data management was successful, resulting in clean, internally consistent data sets for analysis. Study data collection resulted in over 20,000 completed forms. The data management procedures are described. SAS software was used to read the entered data files, check for potential errors, apply corrections to data sets, and combine batches into analytic data sets. Forms were inspected visually and sent off-site for data entry. Data were collected on twenty different forms. Subjects were enrolled, data collected, and follow-ups occurred for over three years.
The Missouri LRI Project was a prospective cohort study of nursing home residents who developed an LRI. Using as an example a large prospective study, the Missouri Lower Respiratory Infection (LRI) Project, we present an approach to data management predominantly using SAS software. Data must be entered, erroneous or missing data must be identified and corrected if possible, and an audit trail created. Maintaining data quality and integrity is important for research studies involving prospective data collection.
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