Statistical Science

Longitudinal Data with Follow-up Truncated by Death: Match the Analysis Method to Research Aims

Brenda F. Kurland, Laura L. Johnson, Brian L. Egleston, and Paula H. Diehr

Full-text: Open access

Abstract

Diverse analysis approaches have been proposed to distinguish data missing due to death from nonresponse, and to summarize trajectories of longitudinal data truncated by death. We demonstrate how these analysis approaches arise from factorizations of the distribution of longitudinal data and survival information. Models are illustrated using cognitive functioning data for older adults. For unconditional models, deaths do not occur, deaths are independent of the longitudinal response, or the unconditional longitudinal response is averaged over the survival distribution. Unconditional models, such as random effects models fit to unbalanced data, may implicitly impute data beyond the time of death. Fully conditional models stratify the longitudinal response trajectory by time of death. Fully conditional models are effective for describing individual trajectories, in terms of either aging (age, or years from baseline) or dying (years from death). Causal models (principal stratification) as currently applied are fully conditional models, since group differences at one timepoint are described for a cohort that will survive past a later timepoint. Partly conditional models summarize the longitudinal response in the dynamic cohort of survivors. Partly conditional models are serial cross-sectional snapshots of the response, reflecting the average response in survivors at a given timepoint rather than individual trajectories. Joint models of survival and longitudinal response describe the evolving health status of the entire cohort. Researchers using longitudinal data should consider which method of accommodating deaths is consistent with research aims, and use analysis methods accordingly.

Article information

Source
Statist. Sci. Volume 24, Number 2 (2009), 211-222.

Dates
First available in Project Euclid: 14 January 2010

Permanent link to this document
https://projecteuclid.org/euclid.ss/1263478382

Digital Object Identifier
doi:10.1214/09-STS293

Mathematical Reviews number (MathSciNet)
MR2655850

Zentralblatt MATH identifier
1328.62574

Keywords
Censoring generalized estimating equations longitudinal data missing data quality of life random effects models truncation by death

Citation

Kurland, Brenda F.; Johnson, Laura L.; Egleston, Brian L.; Diehr, Paula H. Longitudinal Data with Follow-up Truncated by Death: Match the Analysis Method to Research Aims. Statist. Sci. 24 (2009), no. 2, 211--222. doi:10.1214/09-STS293. https://projecteuclid.org/euclid.ss/1263478382.


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