Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Thompson says America must "unchoose" policies that have led to mass incarceration

Axinn says new data on campus rape will "allow students to see for themselves the full extent of this problem"

Frey says white population is growing in Detroit and other large cities


Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

Next Brown Bag

Monday, Oct 5 at noon, 6050 ISR
Colter Mitchell: Biological consequences of poverty

A multiple imputation approach to disclosure limitation for high-age individuals in longitudinal studies

Archived Abstract of Former PSC Researcher

An, D., R.J. Little, and James McNally. 2010. "A multiple imputation approach to disclosure limitation for high-age individuals in longitudinal studies." Statistics in Medicine, 29(17): 1769-1778.

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 with repeated measures over time. Research on disclosure limitation methods for longitudinal data has been very limited. We consider here problems created by high ages in cohort studies. Because of the risk of disclosure, ages of very old respondents can often not be released; in particular, this is a specific stipulation of the Health Insurance Portability and Accountability Act (HIPAA) for the release of health data for individuals. Top-coding of individuals beyond a certain age is a standard way of dealing with this issue, and it may be adequate for cross-sectional data, when a modest number of cases are affected. However, this approach leads to serious loss of information in longitudinal studies when individuals have been followed for many years. 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 (CHS), and is shown to work well in preserving the relationship between hazard and covariates. Copyright (C) 2010 John Wiley & Sons, Ltd.

DOI:10.1002/sim.3974 (Full Text)

PMCID: PMC2910194. (Pub Med Central)

Country of focus: United States of America.

Browse | Search : All Pubs | Next