Multiple Imputation Method for Disclosure Limitation in Longitudinal Data

• Activity Number: 333
• Type: Topic Contributed
• Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Di An*+ and Roderick Joseph Little and James W. McNally
• Companies: Merck & Co., Inc. and University of Michigan and University of Michigan
• Keywords: confidentiality ; disclosure protection ; longitudinal data ; multiple imputation ; survival analysis

Abstract

Disclosure limitation is an important consideration in the release of public use data sets. It is particularly challenging for longitudinal data sets, since information about an individual accumulates over time. We consider problems created by high ages in cohort studies. Because of the risk of disclosure, ages of very old respondents can often not be released, as stipulated by the Health Insurance Portability and Accountability Act (HIPAA). Top-coding of individuals beyond a certain age is a standard way of dealing with this issue, but it has severe limitations in longitudinal studies. We propose and evaluate an alternative to top-coding for this situation based on multiple imputation (MI). This MI method is applied to a survival analysis of simulated data and data from the Charleston Heart Study, and is shown to work well in preserving the relationship between hazard and covariates.