Estimating population-averaged hazard ratios in the presence of unmeasured confounding

Martínez-Camblor Pablo (), MacKenzie Todd A. and O’Malley A. James
Additional contact information
Martínez-Camblor Pablo: Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
MacKenzie Todd A.: Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
O’Malley A. James: Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA

The International Journal of Biostatistics, 2023, vol. 19, issue 1, 39-52

Abstract: The Cox regression model and its associated hazard ratio (HR) are frequently used for summarizing the effect of treatments on time to event outcomes. However, the HR’s interpretation strongly depends on the assumed underlying survival model. The challenge of interpreting the HR has been the focus of a number of recent papers. Several alternative measures have been proposed in order to deal with these concerns. The marginal Cox regression models include an identifiable hazard ratio without individual but populational causal interpretation. In this work, we study the properties of one particular marginal Cox regression model and consider its estimation in the presence of omitted confounder from an instrumental variable-based procedure. We prove the large sample consistency of an estimation score which allows non-binary treatments. Our Monte Carlo simulations suggest that finite sample behavior of the procedure is adequate. The studied estimator is more robust than its competitor (Wang et al.) for weak instruments although it is slightly more biased for large effects of the treatment. The practical use of the presented techniques is illustrated through a real practical example using data from the vascular quality initiative registry. The used R code is provided as Supplementary material.

Keywords: causal effect; Cox regression model; instrumental variable; mis-specified models; omitted covariates; population-averaged hazard ratio (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2021-0096 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:ijbist:v:19:y:2023:i:1:p:39-52:n:10

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2021-0096

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().