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Analysis of Missingness Scenarios for Observational Health Data.
Zamanian, Alireza; von Kleist, Henrik; Ciora, Octavia-Andreea; Piperno, Marta; Lancho, Gino; Ahmidi, Narges.
Afiliação
  • Zamanian A; Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany.
  • von Kleist H; Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany.
  • Ciora OA; Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany.
  • Piperno M; Institute of Computational Biology, Helmholtz Center Munich, 80939 Munich, Germany.
  • Lancho G; Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany.
  • Ahmidi N; Fraunhofer Institute for Cognitive Systems IKS, 80686 Munich, Germany.
J Pers Med ; 14(5)2024 May 11.
Article em En | MEDLINE | ID: mdl-38793096
ABSTRACT
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Pers Med Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Pers Med Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Alemanha