Handling missing data, selection bias, and measurement error in observational studies

Choi, J. (2023)

Doctoral Thesis

This thesis addresses potential threats to the validity of observational epidemiological studies. Examples of these potential sources of bias are confounding, missing data, selection bias, and measurement error. Although various methods have been developed to mitigate these biases, it is often unclear which methods can be used in which empirical settings. It is also common that issues discussed in methodological studies are overlooked in clinical research. Thus, we investigated problems of
missing data, selection bias, and measurement error occurring in several specific observational settings and discuss how to optimally handle them.

• Epidemiology
• Missing data
• Selection bias
• Measurement error
• Medication use
• Casual inference
• Estimand
• Observational study

• All authors: Choi, J.
• Supervisor: Cessie, S. le; Dekkers, O.M.
• Committee: Groenwold, R.H.H.; Rooij, M.J. de; Lingsma, H.F.; Mutsert, R. de
• Qualification: Doctor (dr.)
• Awarding Institution: Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University
• Date: 2023-06-22
• ISBN (print): 9789464831429