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Related Experiment Videos

Double data entry: what value, what price?

S Day1, P Fayers, D Harvey

  • 1Leo Pharmaceuticals, Princes Risborough, Buckinghamshire, United Kingdom.

Controlled Clinical Trials
|March 11, 1998
PubMed
Summary
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Double data entry is not essential for high-quality clinical trial data. Exploratory data analysis, particularly range checks, effectively identifies critical errors, ensuring reliable study results.

Area of Science:

  • Clinical Trials
  • Data Management
  • Biostatistics

Background:

  • Traditional double data entry (DDE) is widely used for ensuring data quality in clinical trials.
  • The necessity and sufficiency of DDE for guaranteeing high-quality clinical trial data are questioned.
  • The impact of poor data quality on clinical trial outcomes, including estimation, significance testing, and statistical power, requires quantification.

Purpose of the Study:

  • To challenge the assumption that double data entry is the sole method for ensuring data quality in clinical trials.
  • To quantify the effects of poor data quality on the results of clinical trials.
  • To evaluate the effectiveness of exploratory data analysis (EDA) methods, specifically range checks, in identifying and correcting data errors.

Main Methods:

Related Experiment Videos

  • Simulated blood pressure measurements with introduced digit errors were used to assess data quality.
  • Simple range checks were implemented to detect errors impacting final study results.
  • Comparison of error detection capabilities between double data entry and exploratory data analysis methods.

Main Results:

  • Simple range checks effectively detect and allow correction of the most critical errors affecting clinical trial conclusions.
  • Errors not easily detected by range checks, though potentially numerous, have minimal impact on statistical analysis and conclusions.
  • Exploratory data analysis can identify errors missed by DDE, and vice versa, highlighting complementary roles.

Conclusions:

  • Double data entry is not strictly necessary or sufficient for ensuring high-quality clinical trial data.
  • Exploratory data analysis methods, especially range checks, are crucial for identifying significant data errors.
  • The mandatory use of EDA, either during or after data entry, is recommended as a primary step in data analysis strategies for clinical trials.