Matched design for marginal causal effect on restricted mean survival time in observational studies

Lin Zihan (), Ni Ai () and Lu Bo ()
Additional contact information
Lin Zihan: Division of Biostatistics, College of Public Health, The Ohio State University, 244 Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, United States
Ni Ai: Division of Biostatistics, College of Public Health, The Ohio State University, 244 Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, United States
Lu Bo: Division of Biostatistics, College of Public Health, The Ohio State University, 244 Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, United States

Journal of Causal Inference, 2023, vol. 11, issue 1, 28

Abstract: Investigating the causal relationship between exposure and time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using hazard ratio (HR) as the marginal causal effect measure due to noncollapsibility. In this article, we advocate using restricted mean survival time (RMST) difference as a marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is generally more robust than outcome modeling due to potential misspecifications. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased, and the corresponding variance estimator is calculated by accounting for the correlation due to matching. Simulation studies also demonstrate that our method has adequate empirical performance and outperforms several competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E-value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival.

Keywords: confounding bias; marginal effect; noncollapsibility; propensity score matching; sensitivity analysis (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2022-0035 (text/html)

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:causin:v:11:y:2023:i:1:p:28:n:1

DOI: 10.1515/jci-2022-0035

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().