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Overcoming challenges to data quality in the ASPREE clinical trial

Citation

Lockery, JE and Collyer, TA and Reid, CM and Ernst, ME and Gilbertson, D and Hay, N and Kirpach, B and McNeil, JJ and Nelson, MR and Orchard, SG and Pruksawongsin, K and Shah, RC and Wolfe, R and Woods, RL, on behalf of the ASPREE Investigator Group, Overcoming challenges to data quality in the ASPREE clinical trial, Trials, 20, (1) Article 686. ISSN 1745-6215 (2019) [Refereed Article]


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Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1186/s13063-019-3789-2

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.

Item Details

Item Type:Refereed Article
Keywords:health data, clinical trial, data quality, health technology
Research Division:Biomedical and Clinical Sciences
Research Group:Cardiovascular medicine and haematology
Research Field:Cardiology (incl. cardiovascular diseases)
Objective Division:Health
Objective Group:Clinical health
Objective Field:Clinical health not elsewhere classified
UTAS Author:Nelson, MR (Professor Mark Nelson)
ID Code:136580
Year Published:2019
Deposited By:Menzies Institute for Medical Research
Deposited On:2020-01-09
Last Modified:2020-03-30
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