Data management for prospective research studies using SAS®software

Robin L Kruse,David R Mehr

doi:10.1186/1471-2288-8-61

Abstract

BackgroundMaintaining data quality and integrity is important for research studies involving prospective data collection. Data must be entered, erroneous or missing data must be identified and corrected if possible, and an audit trail created.MethodsUsing 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. The Missouri LRI Project was a prospective cohort study of nursing home residents who developed an LRI. Subjects were enrolled, data collected, and follow-ups occurred for over three years. Data were collected on twenty different forms. Forms were inspected visually and sent off-site for data entry. 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. The data management procedures are described.ResultsStudy data collection resulted in over 20,000 completed forms. Data management was successful, resulting in clean, internally consistent data sets for analysis. The amount of time required for data management was substantially underestimated.ConclusionData management for prospective studies should be planned well in advance of data collection. An ongoing process with data entered and checked as they become available allows timely recovery of errors and missing data.

Highlights

Maintaining data quality and integrity is important for research studies involving prospective data collection
Facility nurses reported 4,959 illness episodes; after applying evaluation and exclusion criteria (Table 1), 2,592 episodes were eligible for evaluation
Over the course of the study, data collection resulted in 20,500 completed forms, with a combined total of 2,899 variables

Summary

Introduction

Maintaining data quality and integrity is important for research studies involving prospective data collection. Erroneous or missing data must be identified and corrected if possible, and an audit trail created. Data that are highly reliable and complete are essential to unbiased, high-quality research studies [1,2]. 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" [3]. Prospective data collection gives researchers control over the quality of their data. It is essential that researchers develop and implement procedures to minimize data loss, identify concerns soon after data are collected, and detect and correct errors [1,2,4,5]. "No study is better than the quality of the data" [6]

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Discussion

Conclusion