Nonparametric Bayesian Multiple Imputation for Missing Data Due to Mid-Study Switching of Measurement Methods

• Activity Number: 341
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: W.J. Youden Award in Interlaboratory Testing
• Author(s): Lane Burgette* and Jerry Reiter
• Companies: RAND Corporation and Duke University
• Keywords: Dirichlet process ; Fusion ; Gaussian process ; Permutation ; Rank

Abstract

Investigators often change how variables are measured during data-collection, for example, in hopes of obtaining greater accuracy or reducing costs. The resulting data comprise sets of observations measured on two different scales, which complicates interpretation and can create bias in analyses that rely directly on the differentially measured variables. We develop approaches based on multiple imputation for handling mid-study changes in measurement for settings without calibration data, that is, no subjects are measured on both scales. Since the measurements never appear jointly, there is no information in the data about their association. We resolve the problem by making an assumption that each measurement regime accurately ranks the samples but on differing scales, so that, for example, an individual at the qth percentile on one scale should be at about the qth percentile on the other scale. We use this assumption to develop three imputation strategies that flexibly transform measurements made in one scale to measurements made in another. We apply these methods to a study of birth outcomes in which environmental contaminants in mothers' blood samples were measured in two labs.