A Multiple Imputation Approach for the Cumulative Incidence, with Implications for Variance Estimation

Elizabeth C. Chase, Philip S. Boonstra and Jeremy M. G. Taylor

The American Statistician, 2025, vol. 79, issue 3, 291-301

Abstract: We present an alternative approach to estimating the cumulative incidence function that uses nonparametric multiple imputation to reduce the problem to that of estimating a binomial proportion. In the standard competing risks setting, we show mathematically and empirically that our imputation-based estimator is equivalent to the Aalen-Johansen estimator of the cumulative incidence given a sufficient number of imputations. However, our approach allows for the use of a wider variety of methods for the analysis of binary outcomes, including preferred options for uncertainty estimation. While we focus on the cumulative incidence function, the multiple imputation approach likely extends to more complex problems in competing risks.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2025.2453674 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:79:y:2025:i:3:p:291-301

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2025.2453674

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().