Error Rate Database
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Work EntryEncyclopedia of Public Health pp 196-196 Database Error Rate Get Access DefinitionDatabase error rate is a measure of data quality. It error rate calculation is defined as the number of errors divided by the total number
Error Rate Running Record
of data. In practice, estimate of this rate can be obtained by counting the number of errors and error rate statistics dividing it by the total number of verified data, i. e. data sample size. The precision of the estimate of database error rate is associated with data sample size; increasing the error rate definition sample size will raise the precision. An acceptable database error rate should be defined prior to the study beginning, and must be considerably below 1%. Finally, any decision about the error rate depends on the aims of the study. It is often defined at 0.1% level. Database error rate can be reduced through the process of data validation. You
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Data Entry Errors and Data Changes to an Electronic Data Capture Clinical Trial DatabaseJules T. Mitchel, MBA, PhD, Yong Joong Kim, MS, Joonhyuk Choi, BS, Glen Park, http://link.springer.com/10.1007/978-1-4020-5614-7_667 PharmD, Silvana Cappi, MSc, MBA, David Horn, MA, Morgan Kist, and Ralph B. D′Agostino, Jr., PhDJules T. Mitchel, President, Target Health Inc, New York;Contributor Information.Jules T. Mitchel, Target Health Inc, 261 Madison Avenue, 24th Floor, New York, NY 10016 (Email: moc.htlaehtegrat@lehctimseluj).Author information ► Copyright and License information ►Copyright notice and DisclaimerSee other articles in PMC https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777611/ that cite the published article.AbstractMonitoring of clinical trials includes several disciplines, stakeholders, and skill sets. The aim of the present study was to identify database changes and data entry errors to an electronic data capture (EDC) clinical trial database, and to access the impact of the changes. To accomblish the aim, Target e*CRF was used as the EDC tool for a multinational, dose-finding, multicenter, double-blind, randomized, parallel, placebo-controlled trial to investigate efficacy and safety of a new treatment in men with lower urinary tract symptoms associated with benign prostatic hyperplasia. The main errors observed were simple transcription errors from the paper source documents to the EDC database. This observation was to be expected, since every transaction has an inherant error rate. What and how to monitor must be assessed within the risk-based monitoring section of the comprehensive data monitoring plan. With the advent of direct data entry, and the elimination of the requirement to transcribe from a paper source record to an EDC sys
Help Latest content Archive eLetters RSS Home > Volume 3, Issue 5 > Article BMJ Open 2013;3:e002406 doi:10.1136/bmjopen-2012-002406 Health informatics Research Error rates http://bmjopen.bmj.com/content/3/5/e002406.full in a clinical data repository: lessons from the transition to electronic data transfer—a descriptive study Matthew K H Hong1, Henry H I Yao1, John S Pedersen2, Justin S Peters1, Anthony J Costello1, Declan G Murphy1,3, Christopher M Hovens1, Niall M Corcoran1 1Division of Urology, Department of Surgery, University of Melbourne, Royal Melbourne Hospital and the Australian Prostate error rate Cancer Research Centre Epworth, Melbourne, Victoria, Australia 2TissuPath Specialist Pathology, Mount Waverley and Monash University Faculty of Medicine, Melbourne, Victoria, Australia 3Division of Cancer Surgery, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia Correspondence to Dr Matthew Hong; m.k.hong{at}ausdoctors.net Received 26 November 2012 Accepted 9 April 2013 Published 17 May 2013 Next Section Abstract Objective Data errors error rate database are a well-documented part of clinical datasets as is their potential to confound downstream analysis. In this study, we explore the reliability of manually transcribed data across different pathology fields in a prostate cancer database and also measure error rates attributable to the source data. Design Descriptive study. Setting Specialist urology service at a single centre in metropolitan Victoria in Australia. Participants Between 2004 and 2011, 1471 patients underwent radical prostatectomy at our institution. In a large proportion of these cases, clinicopathological variables were recorded by manual data-entry. In 2011, we obtained electronic versions of the same printed pathology reports for our cohort. The data were electronically imported in parallel to any existing manual entry record enabling direct comparison between them. Outcome measures Error rates of manually entered data compared with electronically imported data across clinicopathological fields. Results 421 patients had at least 10 comparable pathology fields between the electronic import and manual records and were selected for study. 320 patients had concordant data between manually entered and el