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College of Arts & Sciences
Department of Statistics


Haiying Chen Colloquium

Tuesday, April 19, 2016 - 2:45pm

Statistics Department Colloquium

Where: LeConte College, Room 210

Speaker: Haiying Chen

Affiliation: Wake Forest University, Department of Biostatistical Sciences

Title:  Multiple Imputation of Gait Speed in 400-meter Walk with Time Constraint in the Presence of Non-completers

Abstract: When a 400-meter (400-m) walk test with time constraint (in 15 minutes) is administered, the analysis of the associated 400-m gait speed can be challenging since some older adults are unable to complete the distance (non-completers). A simplistic method is to use the observed speeds of the non-completers calculated over the distance and time attempted as estimates for the corresponding 400-m gait speeds. This common practice has not been validated to the best of our knowledge. We propose a Bayesian multiple imputation (MI) method to impute the unobserved 400-m gait speed for the non-completers. Briefly, MI is performed under the assumption that the unobserved 400-m gait speed of the non-completers is left-censored from a normal distribution. A Gibbs sampler is used to simulate direct draws from a target distribution using iterative algorithms. We illustrate the application of the Bayesian MI method using longitudinal data collected from the Lifestyle Interventions for Elders (LIFE) study. A simulation study is performed to assess the bias in the estimation of the mean 400-m gait speed using both methods. The results indicate that the simplistic method tends to overestimate the population mean, whereas the Bayesian MI method yields minimal bias as sample size increases.