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Area of Science:

  • Observational Research
  • Data Management
  • Biostatistics

Background:

  • Characterizing missing data patterns is crucial for the validity of observational research.
  • Understanding factors influencing missing data can improve study design and data quality.

Purpose of the Study:

  • To examine the extent and predictors of missing data across diverse observational research settings.
  • To identify specific variables and operational factors associated with data missingness.

Main Methods:

  • Analysis of missing data in three registries: a procedure registry (TOPS), a rare disease registry (Port-CF), and a comparative effectiveness registry (RiGOR).
  • Utilized generalized linear mixed effects models to assess predictors of missingness, including patient characteristics and follow-up methods.
  • Evaluated missingness for demographic, clinical, and patient-reported outcome (PRO) data.

Main Results:

  • Demographic, treatment, and outcome data exhibited low missingness rates.
  • Missingness for clinical variables differed by registry and measure, often influenced by data requirements.
  • Patient-reported outcome (PRO) forms had higher missingness when collected via email versus paper in the RiGOR registry.
  • In the Port-CF registry, missingness varied by insurance status and sex.

Conclusions:

  • Missing data patterns are not uniform and depend on the registry and data type.
  • Operational factors, such as data collection methods (e.g., email vs. paper PROs), significantly impact missingness.
  • Proactive strategic planning and ongoing assessment of data collection operations are essential to minimize missing data in observational studies.