Am J Epidemiol. 2015 Feb 15;181(4):246-50. doi: 10.1093/aje/kwv001. Epub 2015 Feb 5.


The epidemiologist primarily studies transitions between states of health and disease. The purpose of the present article is to define a foundational parameter for such studies, namely risk. We begin simply and build to the setting in which there is more than 1 event type (i.e., competing risks or competing events), as well as more than 1 treatment or exposure level of interest. In the presence of competing events, the risks are a set of counterfactual cumulative incidence functions for each treatment. These risks can be depicted visually and summarized numerically. We use an example from the study of human immunodeficiency virus to illustrate concepts.

Keywords: causal inference; cohort study; semi-Bayes method; semiparametric inference; survival analysis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Anti-HIV Agents / therapeutic use
  • Cohort Studies
  • HIV Infections / drug therapy
  • HIV Infections / mortality*
  • Humans
  • Incidence
  • Mathematical Computing
  • Risk Assessment
  • Risk Factors
  • Time Factors
  • United States / epidemiology


  • Anti-HIV Agents