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Published in: Trials 1/2019

Open Access 01-12-2019 | Methodology

Overcoming challenges to data quality in the ASPREE clinical trial

Authors: Jessica E. Lockery, Taya A. Collyer, Christopher M. Reid, Michael E. Ernst, David Gilbertson, Nino Hay, Brenda Kirpach, John J. McNeil, Mark R. Nelson, Suzanne G. Orchard, Kunnapoj Pruksawongsin, Raj C. Shah, Rory Wolfe, Robyn L. Woods, on behalf of the ASPREE Investigator Group

Published in: Trials | Issue 1/2019

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Abstract

Background

Large-scale studies risk generating inaccurate and missing data due to the complexity of data collection. Technology has the potential to improve data quality by providing operational support to data collectors. However, this potential is under-explored in community-based trials. The Aspirin in reducing events in the elderly (ASPREE) trial developed a data suite that was specifically designed to support data collectors: the ASPREE Web Accessible Relational Database (AWARD). This paper describes AWARD and the impact of system design on data quality.

Methods

AWARD’s operational requirements, conceptual design, key challenges and design solutions for data quality are presented. Impact of design features is assessed through comparison of baseline data collected prior to implementation of key functionality (n = 1000) with data collected post implementation (n = 18,114). Overall data quality is assessed according to data category.

Results

At baseline, implementation of user-driven functionality reduced staff error (from 0.3% to 0.01%), out-of-range data entry (from 0.14% to 0.04%) and protocol deviations (from 0.4% to 0.08%). In the longitudinal data set, which contained more than 39 million data values collected within AWARD, 96.6% of data values were entered within specified query range or found to be accurate upon querying. The remaining data were missing (3.4%). Participant non-attendance at scheduled study activity was the most common cause of missing data. Costs associated with cleaning data in ASPREE were lower than expected compared with reports from other trials.

Conclusions

Clinical trials undertake complex operational activity in order to collect data, but technology rarely provides sufficient support. We find the AWARD suite provides proof of principle that designing technology to support data collectors can mitigate known causes of poor data quality and produce higher-quality data. Health information technology (IT) products that support the conduct of scheduled activity in addition to traditional data entry will enhance community-based clinical trials. A standardised framework for reporting data quality would aid comparisons across clinical trials.

Trial registration

International Standard Randomized Controlled Trial Number Register, ISRCTN83772183. Registered on 3 March 2005.
Appendix
Available only for authorised users
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Metadata
Title
Overcoming challenges to data quality in the ASPREE clinical trial
Authors
Jessica E. Lockery
Taya A. Collyer
Christopher M. Reid
Michael E. Ernst
David Gilbertson
Nino Hay
Brenda Kirpach
John J. McNeil
Mark R. Nelson
Suzanne G. Orchard
Kunnapoj Pruksawongsin
Raj C. Shah
Rory Wolfe
Robyn L. Woods
on behalf of the ASPREE Investigator Group
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Trials / Issue 1/2019
Electronic ISSN: 1745-6215
DOI
https://doi.org/10.1186/s13063-019-3789-2

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