January 16

Designing RCTs and Observational Studies to Account for Missing Data not Missing at Random

Mark McGovern is a Lecturer in Economics at Queen’s Management School, Queen’s University Belfast, and the UKCRC Centre of Excellence for Public Health (Northern Ireland).  Prior to joining Queen’s in September 2015, he was a Program on the Global Demography of Aging Postdoctoral Fellow at Harvard University.  He received his PhD in economics from University College Dublin in 2013.  His main research interests are in health and development, including a variety of topics in ageing, HIV, and maternal/child health.  His work has involved the application of causal inference methods for observational data to research questions in these areas, such as evaluating the impact of early life conditions on child and adult outcomes.  His work has been featured in journals such as Economics and Human Biology, Journal of Population Economics, Journal of Health Economics, Journal of the Economics of Ageing, Journal of the International AIDS Society, Epidemiology, and American Journal of Epidemiology.  Most recently, he has been working on developing methods for dealing with non-ignorable missing data.

Missing data is a common feature of both survey data and RCTs, which has the potential to greatly impact on the policy recommendations we derive from empirical studies. Non-response can lead to biased estimates if the characteristics of respondents systematically differ from those who decline to participate. In practice, if any adjustments for missing data are made, they tend to be based on either multiple imputation or inverse probability weighting. Conventional methods such as these all rely on a key assumption: missing data must be missing at random, or missing at random conditional on observed covariates. This is a strong and generally untestable assumption which is unrealistic in many settings, especially where some respondents have an incentive not to participate. In this presentation, I show how an alternative approach, Heckman-type selection models, can be used for dealing with missing data. This method can provide unbiased estimates even when the assumption of missing at random does not hold, and respondents systematically opt out of survey participation on the basis of unobserved confounders. Using examples from research on HIV, I illustrate the consequences of imposing an unrealistic missing at random assumption on survey data. I conclude by discussing how to design RCTs and observational studies to facilitate the implementation of this selection model approach.

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Understanding rates of work disability in Northern Ireland

Co-supervisors:  Dr Declan French, QMS and Prof Duncan McVicar, QMS with third supervisor Dr Dermot O’Reilly, Centre for Public Health

Project Description:
In Northern Ireland (NI), we have the highest disability claimant rate in the UK and the reasons for this are poorly understood, although it has been variously attributed in policy circles to worse health, worse unemployment hidden as work disablement, the physical and mental health consequences of the recent 30-year civil war known locally as ‘the Troubles’, or to a claimant culture with greater understanding of how to navigate the benefit system from knowledge of entitlement, application, interview, inspection through to eventual successful receipt. Credible empirical evidence that can help us quantify the role of these and other factors, however, is currently lacking. Providing such evidence is the aim of this study.

This 3 year PhD studentship commences in 1 October 2016.

This exciting initiative straddles both economics and health and the successful PhD student will also be able to draw on the medical and epidemiological expertise of the Centre of Excellence (CoE) for Public Health at QUB as well as expertise in data linkage. It is envisaged that the student will participate fully in CoE activities alongside QUMS activities, including those involving other UK Centres of Excellence.

Academic Requirements:

This MRC-funded studentship will be awarded on a competitive basis to outstanding applicants who have:
• An excellent undergraduate degree (graded 2.1 or 1st) in a relevant subject;
• Completed, or are due to complete by September 2016, a Master’s degree in a relevant subject to an excellent standard (graded Merit or Distinction);
Candidates with a background in experience in labour economics, health economics or health policy will be preferred.

Contact details:
Interested candidates are encouraged to contact co-supervisors Dr Declan French, QMS () and Prof Duncan McVicar, QUMS () or third supervisor Dr Dermot O’Reilly, Centre for Public Health ( ) to discuss the project in more detail.

To Apply
The closing date for applications is March 11th 2016. To apply for this studentship (which covers student fees and a maintenance allowance) please go to the online postgraduate application portal below registering your application against Queen’s Management School and the supervisors above


Contact Dr Declan French () if you have any difficulties.

Funding Notes

This studentship covers student fees and a maintenance allowance of £13,863 (2014-15 figures). The studentship may cover maintenance and fees for a maximum of three years. UK residents: fees plus maintenance. Other EU residents: fees only.

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