Reducing quantitative data errors: tips for clinical researchers

Schneider, Joanne Kraenzle; Deenan, Aporn

doi:10.1016/j.apnr.2004.02.001

Cited by 6 publications

(10 citation statements)

References 5 publications

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“…Data were checked for accuracy of entry across the two countries. To minimise data entry errors, the database followed a prescribed codebook (Schneider & Deenan, ). Quality control procedures were followed (King & Lashley, ).…”

Section: Methodsmentioning

confidence: 99%

Family functioning and perceived support from nurses during cancer treatment among Danish and Australian patients and their families

Dieperink

Coyne

Creedy

et al. 2017

Journal of Clinical Nursing

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Section: Methodsmentioning

confidence: 99%

Family functioning and perceived support from nurses during cancer treatment among Danish and Australian patients and their families

Dieperink

Coyne

Creedy

et al. 2017

Journal of Clinical Nursing

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“…The surplus collected data consisted of partially false and partially unnecessary information (data not shown). To reduce data errors, DCs should use protocols [2, 11, 12]. Protocols should be easy to follow, with clear descriptions of what data to collect [3, 11].…”

Section: Discussionmentioning

confidence: 99%

“…Data collectors are divided into core function data collectors and additional job data collectors (HIV consultants and medical secretaries). Data collectors (DCs), supervised by treating physicians, enter the data online into the national ATHENA database, using specific protocols to standardize the data collection and to minimize errors [2, 3]. This database was developed in Oracle Clinical ® (OC), a system specifically designed for the management of clinical trial data that complies with the guidelines of the International Conference on Harmonisation (Good Clinical Practice) and the U.S. Food and Drug Administration.…”

Section: Introductionmentioning

confidence: 99%

“…This database was developed in Oracle Clinical ® (OC), a system specifically designed for the management of clinical trial data that complies with the guidelines of the International Conference on Harmonisation (Good Clinical Practice) and the U.S. Food and Drug Administration. The OC database provides cross-checking options and discrepancy checks, which increases the efficiency and accuracy of data entry [2, 4-7]. Data monitors (DMs) supervise the quality of the data by checking their accuracy and completeness.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the DMs check for the correct use of specific protocols and privacy regulations, and they train the DCs in complete and accurate collection. Regular feedback and training maximises the reliability of the data [2, 8]. Controlling the quality of data obtained from patients is crucial for all clinical research, and source data verification (SDV) is the preferred approach [2-4, 8, 9].…”

Section: Introductionmentioning

confidence: 99%

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Measuring the Quality of Data Collection in a Large Observational Cohort of HIV and AIDS

Hillebregt¹,

Klerk

Knol

et al. 2010

TOAIDJ

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The aim of this study was to examine the quality of data collection by studying the validity of collected data. Data were extracted from the clinic charts of two anonymous outpatients by 38 data collectors. A standard for the data to be collected was determined (168 items). The validity was measured by comparing the collected items with the standard; in this way, the percentages of the collected items that were ‘correct’ could be calculated. The percentage ‘correct’ was higher for clinic chart 1 (mean: 83% correct, SD 7%) than for clinic chart 2 (mean: 78% correct, SD 8%). All categories contained incorrectly collected data. These data were divided into missing data, incorrect start-stop dates, and surplus collected data. Almost all start-stop dates would change into ‘correct’ if ‘monthyear’ was considered correct (instead of the standard ‘daymonthyear’). Not all data collectors used specific protocols, and sources other than the written comments were not always checked. This study shows that a high proportion of data was correctly collected. However, the collection of start-stop dates was not optimal, and the collected data included surplus and missing data. Data collectors should be more knowledgeable about HIV disease and trained in the use of difficult protocols, so that they can better recognize what data to collect and how it should be collected. Among physicians, there should be more agreement about what information to record in the charts, to facilitate data extraction for data collectors.

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