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Overcoming challenges to data quality in the ASPREE clinical trial
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]
Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
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 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 Group:||Clinical health|
|Objective Field:||Clinical health not elsewhere classified|
|UTAS Author:||Nelson, MR (Professor Mark Nelson)|
|Web of Science® Times Cited:||4|
|Deposited By:||Menzies Institute for Medical Research|
|Downloads:||14 View Download Statistics|
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