A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA

Hu, L., Zou, J., Gu, C., Ji, J., Lopez, M., & Kale, M. (2022). A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data. The Annals of Applied Statistics, 16(2). https://doi.org/10.1214/21-aoas1530
Authors:
Liangyuan Hu
Jungang Zou
Chenyang Gu
Jiayi Ji
Michael Lopez
Minal Kale
Affiliated Authors:
Jungang Zou
Columbia Affiliation:
Author Keywords:
causal inference
ignorability assumption
observational data
bayesian inference
nested multiple imputation
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Publication Type:
Article
Unique ID:
10.1214/21-AOAS1530
PMID:
36644682
DOI:
10.1214/21-AOAS1530
Journal:
Annals of Applied Statistics
Publication Date:
Data Source:
Web of Science
Source Link:
View Web of Science Record

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