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Zamanian, A.* ; von Kleist, H. ; Ciora, O.A.* ; Piperno, M.* ; Lancho, G.* ; Ahmidi, N.*

Analysis of missingness scenarios for observational health data.

J. Pers. Med. 14:32 (2024)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Missing Data Analysis ; Missing Data Assumptions ; Missingness Distribution Shift ; Missingness Scenarios ; Observational Health Data; Multiple Imputation; Risk; Prediction; Monotone; Model; Score
ISSN (print) / ISBN 2075-4426
e-ISSN 2075-4426
Zeitschrift Journal of Personalized Medicine
Quellenangaben Band: 14, Heft: 5, Seiten: , Artikelnummer: 32 Supplement: ,
Verlag MDPI
Verlagsort Basel, Switzerland
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie