Address:

MRC Biostatistics Unit,
Institute of Public Health,
University Forvie Site,
Robinson Way,
Cambridge. UK.
CB2 0SR

Telephone Number:

Email Address:

Research Interests:

My current main interest is the treatment of missing data in observational studies, particularly in cohort studies. One major cause of missingness in cohort studies is attrition: individuals dropping out of the study. In addition, data are typically missing for some items even on individuals who remain in the study. The simplest approach to analysing data when some values are missing is to restrict the analysis to complete cases. However, it is known that this can lead to bias unless the data are missing completely at random. Multiple imputation (MI) and inverse probability weighting (IPW) are methods that give consistent estimation under the more general assumption that the data are missing at random. The former requires that the imputation model be correctly specified; the latter, that the missingness model (i.e. the model for the probability that an individual is a complete case) be correctly specified. The more recent, doubly robust methodology offers some protection against misspecified imputation or missingness models.

I am interested particularly in the use of MI and IPW for handling real cohort data, in combining MI and IPW, and in doubly robust estimation methods. I work with epidemiological researchers in a variety of MRC units, with the aim of helping improve the way that missing data are handled.

I also have a long-standing interest in HIV/AIDS epidemiology and injecting drug use epidemiology.

Selected Recent Publications:

S. Seaman, A.J. Copas. Doubly Robust Generalised Estimating Equations for Longitudinal Data. Statistics in Medicine 28: 937-955 (2009).

A.J. Copas, S. Seaman. Bias for the Use of Generalised Estimating Equations to Analyse Incomplete Longitudinal Binary Data. Journal of Applied Statistics (in press)

A.M. Presanis, D. De Angelis, D. Spiegelhalter, S. Seaman, A. Goubar, A.E. Ades. Conflicting Evidence in a Bayesian Synthesis of Surveillance Data to Estimate HIV Prevalence. Journal of the Royal Statistical Society: Series A (Statistics in Society) 171:915-937 (2008).

Copas A.J, Seaman S.Discussion on the paper by Goubar, Ades, De Angelis, McGarrigle, Mercer, Tookey, Fenton and Gill. Journal of the Royal Statistical Society Series A-Statistics in Society 171:568-80 (2008).

S. Seaman, B. Müller-Myhsok. Rapid Simulation of P-values for Product Methods and Multiple-Testing Adjustment in Association Studies. American Journal of Human Genetics, 76, 399 - 408 (2005).

S.R. Seaman, S. Richardson. Equivalence of Prospective and Retrospective Models in the Bayesian Analysis of Case-Control Studies. Biometrika, 91, 15 - 25 (2004).