A multiple imputation approach to disclosure limitation for high-age individuals in longitudinal studies
dc.contributor.author | An, Di | en_US |
dc.contributor.author | Little, Roderick J. A. | en_US |
dc.contributor.author | McNally, James W. | en_US |
dc.date.accessioned | 2010-08-02T17:49:00Z | |
dc.date.available | 2011-03-01T16:26:43Z | en_US |
dc.date.issued | 2010-07-30 | en_US |
dc.identifier.citation | An, Di; Little, Roderick J. A.; McNally, James W. (2010). "A multiple imputation approach to disclosure limitation for high-age individuals in longitudinal studies." Statistics in Medicine 29(17): 1769-1778. <http://hdl.handle.net/2027.42/77532> | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/77532 | |
dc.description.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 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 © 2010 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 141973 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Mathematics and Statistics | en_US |
dc.title | A multiple imputation approach to disclosure limitation for high-age individuals in longitudinal studies | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, 1420 Washington Heights M4045, Ann Arbor, MI 48109-2029, U.S.A. | en_US |
dc.contributor.affiliationum | Institute for Social Research, University of Michigan, 330 Packard Street, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationother | Merck Research Laboratories, Merck & Co., Inc., P.O. Box 1000, Upper Gwynedd, PA 19454, U.S.A. ; Merck Research Laboratories, Merck & Co., Inc., P.O. Box 1000, Upper Gwynedd, PA 19454, U.S.A. | en_US |
dc.identifier.pmid | 20552576 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/77532/1/3974_ftp.pdf | |
dc.identifier.doi | 10.1002/sim.3974 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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