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:

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.

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

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 2012; 31: 1238-1248.

Jackson D, Mason D, White IR and Sutton, S. An exploration of the missing data mechanism in an internet based smoking cessation trial. BMC Research Methodology 2012; 12:157.

Baker R, Jackson D. Meta-analysis inside and outside particle physics: two traditions that should converge? Research Synthesis Methods (to appear).

Jackson D, Baker R and Bowden J. A sensitivity analysis framework for the treatment effect measure used in the meta-analysis of comparative binary data from randomised controlled trials. Statistics in Medicine (to appear).

Jackson D, White IR and Riley D. Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Statistics in Medicine (to appear).