Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Nguyen Trang Quynh"'
Publikováno v:
Journal of Causal Inference, Vol 10, Iss 1, Pp 246-279 (2022)
Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This article provides a systematic explanation of such assumptions. We define five potential outcome types whose
Externí odkaz:
https://doaj.org/article/cb671744f66946098a5d4cf14ce15d0c
Autor:
Nguyen, Trang Quynh
This paper concerns outcome missingness in principal stratification analysis. We revisit a common assumption known as latent ignorability or latent missing-at-random (LMAR), often considered a relaxation of missing-at-random (MAR). LMAR posits that t
Externí odkaz:
http://arxiv.org/abs/2407.13904
Publikováno v:
Biostatistics, 2024
The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects (CACE and NACE), we address outcome missingness of
Externí odkaz:
http://arxiv.org/abs/2312.11136
Autor:
Brantner, Carly Lupton, Nguyen, Trang Quynh, Tang, Tengjie, Zhao, Congwen, Hong, Hwanhee, Stuart, Elizabeth A.
Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the
Externí odkaz:
http://arxiv.org/abs/2303.16299
An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates
Externí odkaz:
http://arxiv.org/abs/2303.05032
Autor:
Brantner, Carly Lupton, Chang, Ting-Hsuan, Nguyen, Trang Quynh, Hong, Hwanhee, Di Stefano, Leon, Stuart, Elizabeth A.
Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effe
Externí odkaz:
http://arxiv.org/abs/2302.13428
In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imp
Externí odkaz:
http://arxiv.org/abs/2301.07066
Autor:
Nguyen, Trang Quynh, Ogburn, Elizabeth L., Schmid, Ian, Sarker, Elizabeth B., Greifer, Noah, Koning, Ina M., Stuart, Elizabeth A.
Publikováno v:
Statistics Surveys. 2023. 17:1-41
This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and model-based prediction) and a simple way of combining them (weighted mod
Externí odkaz:
http://arxiv.org/abs/2102.06048
Publikováno v:
PLOS ONE. 2018. 13(12): e0208795
Background: Randomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect mo
Externí odkaz:
http://arxiv.org/abs/2011.13058
Publikováno v:
Journal of Causal Inference. 2022. 10:246-279
Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This paper provides a systematic explanation of such assumptions. We define five potential outcome types whose me
Externí odkaz:
http://arxiv.org/abs/2011.09537