This article is a U.S. Government work and is in the public domain in the U.S.A.
Estimating standard errors for life expectancies based on complex survey data with mortality follow-up: A case study using the National Health Interview Survey Linked Mortality Files†
Article first published online: 22 MAR 2011
This article is a U.S. Government work and is in the public domain in the U.S.A. Published in 2011 by John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 30, Issue 11, pages 1302–1311, 20 May 2011
How to Cite
Schenker, N., Parsons, V. L., Lochner, K. A., Wheatcroft, G. and Pamuk, E. R. (2011), Estimating standard errors for life expectancies based on complex survey data with mortality follow-up: A case study using the National Health Interview Survey Linked Mortality Files. Statist. Med., 30: 1302–1311. doi: 10.1002/sim.4219
- Issue published online: 2 MAY 2011
- Article first published online: 22 MAR 2011
- Manuscript Accepted: 13 JAN 2011
- Manuscript Revised: 27 DEC 2010
- Manuscript Received: 10 DEC 2009
- balanced repeated replication;
- health disparity;
- life table;
- sample survey;
- Taylor series;
- variance estimation
Life expectancy is an important measure for health research and policymaking. Linking individual survey records to mortality data can overcome limitations in vital statistics data used to examine differential mortality by permitting the construction of death rates based on information collected from respondents at the time of interview and facilitating estimation of life expectancies for subgroups of interest. However, use of complex survey data linked to mortality data can complicate the estimation of standard errors. This paper presents a case study of approaches to variance estimation for life expectancies based on life tables, using the National Health Interview Survey Linked Mortality Files. The approaches considered include application of Chiang's traditional method, which is straightforward but does not account for the complex design features of the data; balanced repeated replication (BRR), which is more complicated but accounts more fully for the design features; and compromise, ‘hybrid’ approaches, which can be less difficult to implement than BRR but still account partially for the design features. Two tentative conclusions are drawn. First, it is important to account for the effects of the complex sample design, at least within life-table age intervals. Second, accounting for the effects within age intervals but not across age intervals, as is done by the hybrid methods, can yield reasonably accurate estimates of standard errors, especially for subgroups of interest with more homogeneous characteristics among their members. Published in 2011 by John Wiley & Sons, Ltd.