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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

Data management for prospective research studies using SAS software.

Robin L Kruse1, David R Mehr

  • 1Department of Family and Community Medicine, University of Missouri School of Medicine, Columbia, MO 65212, USA. kruser@health.missouri.edu

BMC Medical Research Methodology
|September 13, 2008
PubMed
Summary
This summary is machine-generated.

Effective data management is crucial for prospective research. This study highlights the importance of early planning and continuous data checking to ensure data quality and integrity in large-scale projects.

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Area of Science:

  • Epidemiology
  • Health Services Research
  • Data Management

Background:

  • Maintaining data quality and integrity is essential for research involving prospective data collection.
  • Key steps include data entry, error identification and correction, and creating an audit trail.

Purpose of the Study:

  • To present a data management approach for a large prospective study.
  • To illustrate the process using the Missouri Lower Respiratory Infection (LRI) Project as an example.

Main Methods:

  • Utilized SAS software for data management in the Missouri LRI Project, a 3-year prospective cohort study.
  • Data collected on 20+ forms, visually inspected, and entered off-site.
  • SAS used for data validation, error correction, and dataset integration.

Main Results:

  • Successfully managed over 20,000 forms, yielding clean, internally consistent datasets for analysis.
  • The time investment for data management was significantly underestimated.

Conclusions:

  • Thorough planning for data management is critical before prospective data collection begins.
  • An ongoing data entry and checking process facilitates timely error and omission recovery.