Address:

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

Telephone Number:

Email Address:

Research Interests:

Dan is interested in applied mathematics. His primary research interests are meta-analysis and methodology for the analysis of trials with missing data. In particular, his PhD and early research papers involve modelling publication and related biases in meta-analysis, although attention has more recently focused on random effects modelling and data synthesis. Current research interests include multivariate and multiple treatments meta-analysis. Dan is especially interested in modelling data where the assumption that data are missing at random is considered implausible. Applications include trials involving smoking cessation, vascular surgery and mental health.

Selected Recent Publications:

Biggerstaff BJ, Jackson D. The exact distribution of Cochran's heterogeneity statistic in one-way random effects meta-analysis Statistics in Medicine. 2008; 27:6093-6110

Jackson D, Bowden J. A re-evaluation of the 'quantile approximation method' for random effects meta-analysis. Statistics in Medicine 2009. 28(2):338-48

The Fibrinogen Studies Collaboration (Writing commitee: D Jackson and I White). Systematically missing confounders in individual participant data meta-analysis of observational cohort studies. Statistics in Medicine 2009; 28:1218-1237

Jackson D, White IR and Thompson SG. Extending DerSimonian and Laird's methodology to perform multivariate random effects meta-analyses. Statistics in Medicine 2009; 29:1282-1297.

Baker R, Jackson D. Inference for meta-analysis with a suspected temporal trend. Biometrical Journal 2010; 52: 538-551.

Jackson D, White IR and Leese M. How much can we learn about missing data?: an exploration of a clinical trial in psychiatry. JRSS Series A 2010; 173: 593-612.

Jackson D, White IR and Riley R. Multivariate meta-analysis: Potential and Promise (with discussion). Statistics in Medicine 2011; 30: 2481-2510.

Jackson D, White IR and Carpenter J. Identifying influential observations in Bayesian models by using Markov chain Monte Carlo. Statistics in Medicine (to appear).