FCCC LOGO Faculty Publications
Kurland BF , Johnson LL , Egleston BL , Diehr PH
Longitudinal Data with Follow-up Truncated by Death: Match the Analysis Method to Research Aims
Stat Sci. 2009 ;24(2) :211
Back to previous list
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.
Notes
N01 HC015103/NHLBI NIH HHS/United States N01 HC035129/NHLBI NIH HHS/United States N01 HC045133/NHLBI NIH HHS/United States N01 HC055222/NHLBI NIH HHS/United States N01 HC075150/NHLBI NIH HHS/United States N01 HC085079/NHLBI NIH HHS/United States N01 HC085080/NHLBI NIH HHS/United States N01 HC085081/NHLBI NIH HHS/United States N01 HC085082/NHLBI NIH HHS/United States N01 HC085083/NHLBI NIH HHS/United States N01 HC085084/NHLBI NIH HHS/United States N01 HC085085/NHLBI NIH HHS/United States U01 HL080295-05/NHLBI NIH HHS/United States Journal article Statistical science : a review journal of the Institute of Mathematical Statistics Stat Sci. 2009;24(2):211.