Journal
AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 181, Issue 4, Pages 246-250Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwv001
Keywords
causal inference; cohort study; semi-Bayes method; semiparametric inference; survival analysis
Categories
Funding
- National Institutes of Health (NIH) [R01AI100654, R24AI067039, U01AI103390, P30AI50410]
- NIH [R01AI100654, P30AI50410, DP2HD084070, R01AG042845, R21HD080214, R01AG023178]
- Agency for Healthcare Research and Quality's Developing Evidence to Inform Decisions About Effectiveness program
- Patient Centered Outcomes Research Institute
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
