About the event

00:00 Introduction
11:39 Missing data theory
40:14 Introduction to CLS cohorts
56:45 CLS Missing Data Strategy
2:01:14 Demonstration (using Stata)
2:33:22 Q & A session

Slides – all open as a PDF on the CLS website

0. Introduction Richard Silverwood
1. Missing Data Theory George Ploubidis
2 Introduction To CLS Cohorts Brian Dodgeon
3. CLS Missing Data Strategy Richard Silverwood
4. Missing Data Strategy Practical Martina Narayanan

View to learn why principled methods of missing data handling are usually required to obtain unbiased estimates in long-running cohort studies, learn how to undertake such analyses, and observe a demonstration of how to do so in practice using Stata, with a focus on multiple imputation. The 1958 National Child Development Study (NCDS) will be used as the main case study, with Next Steps, the 1970 British Cohort Study (BCS70) and the recent COVID-19 Surveys across five national longitudinal cohort studies also discussed.

Learning objectives

By the end of the webinar, you will:

1. Understand why principled methods of missing data handling are usually required to obtain unbiased estimates in long-running cohort studies.

2. Understand why and how variables other than those included in the analysis should be included in principled methods of missing data handling.

3. Have gained familiarity with examples in which principled methods of missing data handling have been applied.

4. Have observed how such approaches could be applied in practice through example applications using data from the NCDS (focusing on the use of multiple imputation in Stata).

This workshop will be run by staff from UCL Centre for Longitudinal Studies and MRC Unit for Lifelong Health and Ageing:

> Prof George Ploubidis, Professor of Population Health and Statistics and Director of NCDS and BCS70
> Dr Richard Silverwood, Associate Professor in Statistics and CLS Chief Statistician
> Dr Martina Narayanan, Research Associate
> Brian Dodgeon, Research Fellow
> Dr Michalis Katsoulis, Associate Professor of Biomedical Statistics

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