Handling missing data in the 1970 British Cohort Study

6 Jun 2024

This free webinar will provide guidance on how to handle missing data in the 1970 British Cohort Study.

Date Thursday 6 June 2024, 1:00pm - 2:00pm (UK time)
Format Online, using Microsoft Teams

Via the MS Forms registration page. Please note you will NOT receive an auto confirmation on booking. Joining instructions will be sent nearer to the event date.

About the event

This webinar is aimed at users of the 1970 British Cohort Study (BCS70), though may be of interest to users of other longitudinal surveys.

Missing data are common in longitudinal surveys, particularly due to attrition over waves of data collection, and can lead to substantial bias. Principled methods of missing data handling are usually required to obtain unbiased estimates in such settings.

In this webinar we will briefly cover the relevant missing data theory before discussing missing data methods and their application. There will be a particular emphasis on why and how variables other than those required for the analysis should be included in missing data handling.

We will present findings from our recent work in BCS70 and provide guidance for how users of data from this study can handle missing data in their own analyses.

We will demonstrate that with careful analysis it is possible to largely restore sample representativeness despite the presence of selective attrition.

This online webinar is free to attend and will be conducted live, with an opportunity to ask questions. We’ll also publish a recording of the webinar on our YouTube channel without the Q&A.


Michalis Katsoulis is an Associate Professor in Biomedical Statistics at the MRC unit for Lifelong Health and Ageing. He has extensive experience in utilizing both cohort studies and Electronic Health Records. His research focuses on survival analysis, missing data and causal inference from observational data.  

Richard Silverwood is Associate Professor of Statistics and Chief Statistician at the Centre for Longitudinal Studies at UCL. His applied research is mainly within the context of health, and his methodological interests include approaches for handling missing data, the analysis of linked survey and administrative data, and making causal inferences from observational data. 



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