title: Adaptive double data entry: a probabilistic tool for choosing which forms to reenter.
abstract: Several recent articles have supported differing opinions about the value and cost of double data entry in specific clinical trials. The cost of the reentry, combined with the low error rate typical in controlled clinical trials suggests to some that single data entry may be sufficient, with the cost of reentry allocated to more productive quality assurance tools. In this article, an alternative approach to limiting costs and maintaining the quality of entered data is offered. The technique is a formal, adaptive method for choosing a subset of forms to be reentered. The idea behind the approach is to decide whether a given form should be reentered on a form-by-form basis for each data entry person, using an estimated probability that the form contains an error as a guideline. The method automatically adapts to each data entry person and to temporal changes in accuracy within data entry person. The estimated probability is based on a lagged set of the most recently double-data-entered forms. A simple simulation shows that much of the reentry can be avoided while still detecting many of the errors. A real data example demonstrates that the procedure can be effective in practice as well. Control Clin Trials 2001;22:2-12
authors.affiliation: Harvard Medical School and Harvard Pilgrim Health Care, Department of Ambulatory Care and Prevention, Boston, MA 02115, USA. ken_kleinman@hphc.org
authors.forename: K
authors.initials: K
authors.lastname: Kleinman
mesh_terms: D000465:Algorithms; D015982:Bias; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003625:Data Collection / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D011336:Probability