Data quality assurance and control in cognitive research: Lessons learned from the PREDICT-HD study

Holly James Westervelt; Rachel A Bernier; Melanie Faust; Mary Gover; H Jeremy Bockholt; Roland Zschiegner; Jeffrey D Long; Jane S Paulsen; PREDICT-HD Investigators; Coordinators of the Huntington Study Group

doi:10.1002/mpr.1534

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.

Authors

Holly James Westervelt^{1

2}, Rachel A Bernier^{2

3}, Melanie Faust^{2

4

5}, Mary Gover², H Jeremy Bockholt^{6

7}, Roland Zschiegner⁶, Jeffrey D Long^{6

8}, Jane S Paulsen^{6

9

10}; PREDICT-HD Investigators; Coordinators of the Huntington Study Group

Affiliations

¹ Department of Neurology, Vanderbilt Medical Center, Nashville, TN, USA.
² Department of Psychiatry, Rhode Island Hospital, Providence, RI, USA.
³ Department of Psychology, Pennsylvania State University, University Park, PA, USA.
⁴ Graduate School of Nursing, University of Massachusetts Medical School, Worcester, MA, USA.
⁵ Department of Neurology, Butler Hospital, Providence, RI, USA.
⁶ Department of Psychiatry, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA.
⁷ Advanced Biomedical Informatics Group, LLC, Coralville, IA, USA.
⁸ Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, IA, USA.
⁹ Department of Neurology, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA.
¹⁰ Department of Psychology, The University of Iowa, Iowa City, IA, USA.

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*

Abstract

Publication types

MeSH terms

Grants and funding