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. 2011 Jan;66(1):109-14.
doi: 10.1093/gerona/glq188. Epub 2010 Oct 28.

Treatment of death in the analysis of longitudinal studies of gerontological outcomes

T E Murphy  1 , L Han, H G Allore, P N Peduzzi, T M Gill, H Lin
Affiliations

Treatment of death in the analysis of longitudinal studies of gerontological outcomes

T E Murphy et al. J Gerontol A Biol Sci Med Sci. 2011 Jan.

Abstract

Background: Longitudinal studies in gerontology are characterized by termination of measurement from death. Death is related to many important gerontological outcomes, such as functional disability, and may, over time, change the composition of an older study population. For these reasons, treating death as noninformative censoring of a longitudinal outcome may result in biased estimates of regression coefficients related to that outcome.

Methods: In a longitudinal study of community-living older persons, we analytically and graphically illustrate the dependence between death and functional disability. Relative to survivors, decedents display a rapid decline of functional ability in the months preceding death. Death's strong relationship with functional disability demonstrates that death is not independent of this outcome and, hence, leads to informative censoring. We also demonstrate the "healthy survivor effect" that results from death's selection effect, with respect to functional disability, on the longitudinal makeup of an older study population.

Results: We briefly survey commonly used approaches for longitudinal modeling of gerontological outcomes, with special emphasis on their treatment of death. Most common methods treat death as noninformative censoring. However, joint modeling methods are described that take into account any dependency between death and a longitudinal outcome.

Conclusions: In longitudinal studies of older persons, death is often related to gerontological outcomes and, therefore, cannot be safely assumed to represent noninformative censoring. Such analyzes must account for the dependence between outcomes and death as well as the changing nature of the cohort.

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Figures

Figure 1.
Figure 1.
(A) Average activities of daily living (ADLs) disability in those younger than 85 years. (B) Average ADL disability in those aged 85 years or older. The p values in each panel correspond to the overall death effect over time from general estimating equation (GEE) Poisson models of number of ADL disabilities.
See this image and copyright information in PMC

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