Data Quality Assurance and Control in Cognitive Research: Lessons Learned From the PREDICT-HD Study

Int J Methods Psychiatr Res. 2017 Sep;26(3):e1534. doi: 10.1002/mpr.1534. Epub 2017 Feb 17.

Abstract

We discuss the strategies employed in data quality control and quality assurance for the cognitive core of Neurobiological Predictors of Huntington's Disease (PREDICT-HD), a long-term observational study of over 1,000 participants with prodromal Huntington disease. In particular, we provide details regarding the training and continual evaluation of cognitive examiners, methods for error corrections, and strategies to minimize errors in the data. We present five important lessons learned to help other researchers avoid certain assumptions that could potentially lead to inaccuracies in their cognitive data.

Keywords: cognitive assessment; quality assurance; quality control.

Publication types

  • Multicenter Study
  • Observational Study
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Biomedical Research / standards*
  • Cognitive Dysfunction / diagnosis*
  • Cognitive Dysfunction / etiology
  • Data Accuracy*
  • Humans
  • Huntington Disease / complications
  • Huntington Disease / diagnosis*
  • Longitudinal Studies
  • Neuropsychological Tests / standards*
  • Prodromal Symptoms*
  • Prognosis
  • Quality Control*