title: Double data entry: what value, what price?
abstract: We challenge the notion that double data entry is either sufficient or necessary to ensure good-quality data in clinical trials. Although we do not completely reject that notion, we quantify some of the effects that poor quality data have on final study results in terms of estimation, significance testing, and power. By introducing digit errors into simulated blood pressure measurements we demonstrate that simple range checks allow us to detect (and therefore correct) the main errors that impact the final study results and conclusions. The errors that cannot easily be detected by such range checks, although possibly numerous, are shown to be of little importance in drawing the correct conclusions from the statistical analysis of data. Exploratory data analysis cannot identify all errors that a second data entry would detect, but on the other hand, not all errors that are found by exploratory data analysis are detectable by double data entry. Double data entry is concerned solely with ensuring, to a high degree of certainty, that what is recorded on the case record form is transcribed into the database. Exploratory data analysis looks beyond the case record form to challenge the plausibility of the written data. In this sense, the second entering of data has some benefit, but the use of exploratory data analysis methods, either as data entry is ongoing or at the end of data entry and as the first stage in an analysis strategy, should always be mandatory.
authors.affiliation: Leo Pharmaceuticals, Princes Risborough, Buckinghamshire, United Kingdom.
authors.forename: S
authors.initials: S
authors.lastname: Day
authors.forename: P
authors.initials: P
authors.lastname: Fayers
authors.forename: D
authors.initials: D
authors.lastname: Harvey
mesh_terms: D000465:Algorithms; D015982:Bias; D001794:Blood Pressure; D001795:Blood Pressure Determination / Q000706:statistics & numerical data; D002986:Clinical Trials as Topic; D003625:Data Collection / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D019992:Databases as Topic; D006801:Humans; D008499:Medical Records; D015233:Models, Statistical; D015203:Reproducibility of Results