Applications of Resampling and Bootstrap Methods to Estimate Prediction Intervals for Nonlinked Replicates in Method Comparison Studies

• Activity Number: 375
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Maya Sternberg*+ and Sharon Flores
• Companies: Centers for Disease Control & Prevention and Centers for Disease Control and Prevention
• Keywords: Method Comparison ; Resampling ; Prediction linterval

Abstract

The Bland-Altman plot, with its limits of agreement, has been a staple data analytic approach for method comparison studies in clinical chemistry. While straightforward, this approach becomes challenging for more complicated designs; such as having more than 2 methods and/or replicates from the same specimen or subject. Since the limits of agreement are equivalent to a prediction interval for the difference between two methods given the same specimen or subject, this statistical problem can easily be cast as a linear mixed model and extended to more complicated designs. For example, in clinical chemistry replicate measurements are made both within and across different runs and replicates from the same specimen may be prepared by different chemists. The mixed model approach allows the method comparison to be folded into a broader question describing the components of variance. However, often in these more complicated designs the replicates are not linked in an obvious way across each specimen and method. This presentation demonstrates the use resampling and bootstrap methods to estimate prediction intervals for non-linked replicates using real data.