Lifetime Data Analysis

, Volume 20, Issue 4, pp 538–562

Semicompeting risks in aging research: methods, issues and needs


DOI: 10.1007/s10985-014-9295-7

Cite this article as:
Varadhan, R., Xue, QL. & Bandeen-Roche, K. Lifetime Data Anal (2014) 20: 538. doi:10.1007/s10985-014-9295-7


A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class \(\mathcal {O}\)) and those based on potential (latent) failure times (class \(\mathcal {L}\)). The classical illness-death model belongs to class \(\mathcal {O}\). This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times (\(\mathcal {L}\) models). These advances notwithstanding, the semicompeting risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community.


Competing risksSemicompeting processesMulti-state modelsInformative censoringPotential failure timesCausal inference

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ravi Varadhan
    • 1
  • Qian-Li Xue
    • 1
  • Karen Bandeen-Roche
    • 2
  1. 1.Division of Geriatric Medicine and Gerontology, The Center on Aging and HealthJohns Hopkins UniversityBaltimoreUSA
  2. 2.Department of Biostatistics, Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreUSA