B4189 - Methods to Detect and Adjust for Selection Bias - 14/11/2022

B number: 
Principal applicant name: 
Apostolos Gkatzionis | MRC Integrative Epidemiology Unit, University of Bristol (United Kingdom)
Professor Kate Tilling
Title of project: 
Methods to Detect and Adjust for Selection Bias
Proposal summary: 

Biomedical research is often hindered by the presence of missing data. For example, missing data can occur due to study participants' unwillingness to disclose sensitive information about themselves (e.g. refusing to answer questions related to their mental health, smoking habits, alcohol consumption or drug use). In our research, we will develop novel statistical methodologies to assess the impact of missing data in epidemiologic studies, understand under which conditions the presence of missing data will bias an applied analysis, and overcome this form of bias.

Impact of research: 
Our research is of methodological nature and will not have a direct impact on medical practice. However, we hope that it will be helpful to applied researchers working with datasets that contain missing data, or are subject to selection bias. This may include other ALSPAC users.
Date proposal received: 
Wednesday, 2 November, 2022
Date proposal approved: 
Monday, 14 November, 2022
Statistics/methodology, Diseases/conditions/variables for which data are requested: obesity (body mass index), alcohol consumption, smoking, depression, self-harm. Also pregnancy-related variables. (Listed under "other" as the list does not allow for selecting multiple diseases/conditions), Statistical methods, Birth outcomes