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Estimation of complier causal treatment effects under the additive hazards model with interval-censored data.
- Source :
-
Communications in Statistics: Theory & Methods . 2024, Vol. 53 Issue 10, p3547-3567. 21p. - Publication Year :
- 2024
-
Abstract
- Estimation of causal treatment effects has attracted a great deal of interest in many areas including social, biological and health science, and for this, instrumental variable (IV) has become a commonly used tool in the presence of unmeasured confounding. In particular, many IV methods have been developed for right-censored time-to-event outcomes. In this paper, we consider a much more complicated situation where one faces interval-censored time-to-event outcomes, which are ubiquitously present in studies with, for example, intermittent follow-up but are challenging to handle in terms of both theory and computation. A sieve maximum likelihood estimation procedure is proposed for estimating complier causal treatment effects under the additive hazards model, and the resulting estimators are shown to be consistent and asymptotically normal. A simulation study is conducted to evaluate the finite sample performance of the proposed approach and suggests that it works well in practice. It is applied to a breast cancer screening study. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03610926
- Volume :
- 53
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Theory & Methods
- Publication Type :
- Academic Journal
- Accession number :
- 176477118
- Full Text :
- https://doi.org/10.1080/03610926.2022.2155791