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Overcoming challenges to data quality in the ASPREE clinical trial.

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.

SUBMITTER: Lockery JE

PROVIDER: S-EPMC6902598 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Overcoming challenges to data quality in the ASPREE clinical trial.

Lockery Jessica E JE Collyer Taya A TA Reid Christopher M CM Ernst Michael E ME Gilbertson David D Hay Nino N Kirpach Brenda B McNeil John J JJ Nelson Mark R MR Orchard Suzanne G SG Pruksawongsin Kunnapoj K Shah Raj C RC Wolfe Rory R Woods Robyn L RL

Trials 20191209 1

<h4>Background</h4>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). Th ...[more]

PMID: 31815652

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Project description:BackgroundUnderstanding the nature of transitions from a healthy state to chronic diseases and death is important for planning health-care system requirements and interventions. We aimed to quantify the trajectories of disease and disability in a population of healthy older people.MethodsWe conducted a secondary analysis of data from the ASPREE trial, which was done in 50 sites in Australia and the USA and recruited community-dwelling, healthy individuals who were aged 70 years or older (≥65 years for Black and Hispanic people in the USA) between March 10, 2010, and Dec 24, 2014. Participants were followed up with annual face-to-face visits, biennial assessments of cognitive function, and biannual visits for physical function until death or June 12, 2017, whichever occurred first. We used multistate models to examine transitions from a healthy state to first intermediate disease events (ie, cancer events, stroke events, cardiac events, and physical disability or dementia) and, ultimately, to death. We also examined the effects of age and sex on transition rates using Cox proportional hazards regression models.Findings19 114 participants with a median age of 74·0 years (IQR 71·6-77·7) were included in our analyses. During a median follow-up of 4·7 years (IQR 3·6-5·7), 1933 (10·1%) of 19 114 participants had an incident cancer event, 487 (2·5%) had an incident cardiac event, 398 (2·1%) had an incident stroke event, 924 (4·8%) developed persistent physical disability or dementia, and 1052 (5·5%) died. 15 398 (80·6%) individuals did not have any of these events during follow-up. The highest proportion of deaths followed incident cancer (501 [47·6%] of 1052) and 129 (12·3%) participants transitioned from disability or dementia to death. Among 12 postulated transitions, transitions from the intermediate states to death had much higher rates than transitions from a healthy state to death. The progression rates to death were 158 events per 1000 person-years (95% CI 144-172) from cancer, 112 events per 1000 person-years (86-145) from stroke, 88 events per 1000 person-years (68-111) from cardiac disease, 69 events per 1000 person-years (58-82) from disability or dementia, and four events per 1000 person-years (4-5) from a healthy state. Age was significantly associated with an accelerated rate for most transitions. Male sex (vs female sex) was significantly associated with an accelerate rate for five of 12 transitions.InterpretationWe describe a multistate model in a healthy older population in whom the most common transition was from a healthy state to cancer. Our findings provide unique insights into the frequency of events, their transition rates, and the impact of age and sex. These results have implications for preventive health interventions and planning for appropriate levels of residential care in healthy ageing populations.FundingThe National Institutes of Health.

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Overcoming challenges to data quality in the ASPREE clinical trial.

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Overcoming challenges to data quality in the ASPREE clinical trial.

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