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Zamanian, A.* ; Ahmidi, N. ; Drton, M.*

Assessable and interpretable sensitivity analysis in the pattern graph framework for nonignorable missingness mechanisms.

Stat. Med. 42, 5419-5450 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
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The pattern graph framework solves a wide range of missing data problems with nonignorable mechanisms. However, it faces two challenges of assessability and interpretability, particularly important in safety-critical problems such as clinical diagnosis: (i) How can one assess the validity of the framework's a priori assumption and make necessary adjustments to accommodate known information about the problem? (ii) How can one interpret the process of exponential tilting used for sensitivity analysis in the pattern graph framework and choose the tilt perturbations based on meaningful real-world quantities? In this paper, we introduce Informed Sensitivity Analysis, an extension of the pattern graph framework that enables us to incorporate substantive knowledge about the missingness mechanism into the pattern graph framework. Our extension allows us to examine the validity of assumptions underlying pattern graphs and interpret sensitivity analysis results in terms of realistic problem characteristics. We apply our method to a prevalent nonignorable missing data scenario in clinical research. We validate and compare our method's results of our method with a number of widely-used missing data methods, including Unweighted CCA, KNN Imputer, MICE, and MissForest. The validation is done using both boot-strapped simulated experiments as well as real-world clinical observations in the MIMIC-III public dataset.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Interpretability ; Nonignorable Missing Data ; Pattern Graph ; Safety Critical ; Sensitivity Analysis
ISSN (print) / ISBN 0277-6715
e-ISSN 1097-0258
Journal Statistics in Medicine
Quellenangaben Volume: 42, Issue: 29, Pages: 5419-5450 Article Number: , Supplement: ,
Publisher Wiley
Publishing Place 111 River St, Hoboken 07030-5774, Nj Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Bavarian Ministry for Economic Affairs, Regional Development and Energy