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Biostatistics Advance Access published online on September 15, 2006
Biostatistics, doi:10.1093/biostatistics/kxl027
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received June 27, 2005
Revised September 1, 2006
Accepted September 13, 2006

Article

Causal inference for non-mortality outcomes in the presence of death

Brian L. Egleston 1 *, Daniel O. Scharfstein 2, Ellen E. Freeman 3, and Sheila K. West 3

1 Biostatistics Facility, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111
2 Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205
3 Department of Ophthalmology, Johns Hopkins University, Baltimore, MD 21205

* To whom correspondence should be addressed.
Brian L. Egleston, E-mail: Brian.Egleston{at}fccc.edu


  Abstract

Evaluation of the causal effect of a baseline exposure on a morbidity outcome at a fixed time point is often complicated when study participants die before morbidity outcomes are measured. In this setting, the causal effect is only well-defined for the principal stratum of subjects who would live regardless of the exposure. Motivated by gerontologic researchers interested in understanding the causal effect of vision loss on emotional distress in a population with a high mortality rate, we investigate the effect among those who would live both with and without vision loss. Since this subpopulation is not readily identifiable from the data and vision loss is not randomized, we introduce a set of scientifically driven assumptions to identify the causal effect. Since these assumptions are not empirically verifiable, we embed our methodology within a sensitivity analysis framework. We apply our method using the first three rounds of survey data from the Salisbury Eye Evaluation, a population-based cohort study of older adults. We also present a simulation study that validates our method.


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