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Activity Number: 142
Type: Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #316102
Title: Feasibility of Reformatting Data for Multiple Imputation of Clustered Data
Author(s): Kristopher Kapphahn* and Maya Mathur and Maria Montez-Rath and Manisha Desai
Companies: Stanford University and Stanford University School of Medicine and Stanford University School of Medicine and Stanford University
Keywords: missing data ; imputation ; longitudinal data ; computation ; simulation ; time varying covariates
Abstract:

Medical studies are frequently afflicted with missing values. Multiple imputation (MI) is one solution for addressing missing data with broad applicability. Certain data structures present unique challenges to implementing MI. One such structure arises in data sets with time-varying covariates. "Proper" imputation should emulate the scientific model driving the analysis. Incorporating correlation among observations within a subject over time can be a challenge if the scientific model assumes observations are independent and then adjusts the standard errors for clustering through robust estimation. We focus on the feasibility of a widely used approach where each cluster's observations are transformed from long to wide format and stacked together prior to imputation. The scientific model is then fit after transforming back to a long format. We explore the effect of several parameters, including the number of observations per cluster, the number of variables, the intra-class correlation coefficient, and the percentage of missing observations on the following: bias, coverage, mean squared error, and computation time. Our study informs guidelines for applying this MI approach.


Authors who are presenting talks have a * after their name.

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