Workshop on Adjusting for Non-Ignorable Missing Data using Heckman-Type Selection Models

Workshop on Adjusting for Non-Ignorable Missing Data using Heckman-Type Selection Models

Harvard University, September 8^th 2015 0900 – 1800

Background

Missing data is common problem in survey data, and standard approaches for dealing with this issue rely on the strong and generally untestable assumption that data are ignorable (missing at random) once we condition on the observed characteristics of respondents. The assumption of missing at random is often implausible, including in contexts where the outcome itself may be a predictor of survey participation. For example, estimates of HIV prevalence which rely on data collected from blood tests taken from respondents in nationally representative household surveys may be affected by selection bias if those who are HIV positive are less likely to participate in testing. Then, conventional adjustments for missing data, such as using imputation or inverse-probability weighting, will result in biased estimates because of an incorrect assumption of missing at random. Standard approaches are also likely to result in confidence intervals which are too narrow because they ignore the uncertainty surrounding the unknown relationship between participation and the outcome, which needs to be estimated.    

Workshop

This workshop will introduce the use of Heckman-type Selection models for adjusting for non-ignorable missing data with the goal of making this approach easily accessible to researchers working with survey data affected by non-participation. A non-technical introduction to different approaches for dealing with missing data will be provided, and we will discuss the implications of not correctly adjusting for missing data which are not missing at random. We will provide an overview of the statistical rationale for the use of selection models, and the R package SemiParBIVProbit will be presented. This software allows researchers to implement this approach in a straightforward and transparent manner in a variety of different contexts affected by missing data. A simulation study will also be used to demonstrate the properties of the model. The final session will be interactive where participants are invited to bring their own datasets, and the audience and presenters will work together on implementing this approach in their own research. Alternatively, the organizers will provide example data. Throughout, we will illustrate the key concepts using data from HIV research.

Invitation

The workshop is free and open to all interested parties, however space is limited so if you would like to attend please register with Mark McGovern (mcgovern@hsph.harvard.edu). The workshop will take place at Harvard on September 8^th, exact location to be confirmed. Unfortunately we do not have the funds to cover expenses of participants.

Organizers

Harvard University: Till Bärnighausen, Guy Harling, Mark McGovern

University College London: Giampiero Marra

University of London Birbeck: Rosalba Radice

Agenda

Time	Topic
0900-0930	Introductions and Background
0930-1015	Implications of Non-Ignorable Missing Data for Parameter Estimates
1015-1030	Break
1030-1130	Introduction to Selection Models
1130-1230	Overview of Applications of Selection Models
1230-1300	Lunch
1300-1330	Optional Session on Getting Started with R
1330-1415	Introduction to R Package SemiParBIVProbit
1415-1445	Simulation Studies
1445-1500	Break
1500-1800	Interactive session with Data from Participants or Data Provided by Organizers

Key References

Bärnighausen, T., Bor, J., Wandira-Kazibwe, S., & Canning, D. (2011). Correcting HIV Prevalence Estimates for Survey Nonparticipation using Heckman-type Selection Models. Epidemiology, 22(1), 27-35. http://www.ncbi.nlm.nih.gov/pubmed/21150352

 

Marra, G., Radice, R., Till, B., Wood, S., McGovern, M., 2015. A Unified Modeling Approach to Estimating HIV Prevalence in Sub-Saharan African Countries. Research Report 324, Department of Statistical Science, University College London. http://www.ucl.ac.uk/statistics/research/pdfs/rr324.pdf

McGovern, M., Bärnighausen, T., Marra, G., Radice, R., 2015. On the Assumption of Bivariate Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence. Epidemiology 26, 229–327. http://www.ncbi.nlm.nih.gov/pubmed/25643102

 

Marra, Giampiero, and Rosalba Radice, 2015. A Regression Modeling Framework for Analyzing Bivariate Binary Data: The R Package SemiParBIVProbit. http://www.homepages.ucl.ac.uk/~ucakgm0/SemiParB.pdf

 

McGovern, M. E., Bärnighausen, T., Salomon, J. A., & Canning, D. (2015). Using Interviewer Random Effects to Remove Selection Bias from HIV Prevalence Estimates. BMC Medical Research Methodology, 15(1), 8. http://www.biomedcentral.com/1471-2288/15/8/