Many practical difficulties arise in the analysis of clinical research and epidemiological studies: data are incomplete, patients in clinical trials deviate from their allocated treatment, studies in meta-analyses are heterogeneous and inconsistent, and risk models are developed from small numbers of individuals. These difficulties are often ignored or handled weakly, which can lead to poor conclusions being drawn or to controversy undermining a study's impact. We work on finding methods that are well-grounded in statistical theory but which can be applied widely and conveniently in practice. This involves development of methods, writing user-friendly computer software, working with applied collaborators and disseminating good methods through short courses and tutorial articles.

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Rapsomaniki, E., White, I. R., Wood, A. M. & Thompson, S. G. (2012). the Emerging Risk Factors Collaboration. A framework for comparing prognostic models based on net health benefits. Statistics in Medicine 31, 114–130. http://dx.doi.org/10.1002/sim.4362

Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M. & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. British Medical Journal 338, b2393. http://dx.doi.org/10.1136/bmj.b2393

White, I. R. (2009). Multivariate random-effects meta-analysis. Stata Journal 9, 40–56. http://www.stata.com/news/mais.html

White, I. R., Horton, N. J., Carpenter, J. & Pocock, S. J. (2011). Strategy for intention to treat analysis in randomised trials with missing outcome data. British Medical Journal 342, d40. http://dx.doi.org/10.1136/bmj.d40

White, I. R., Wood, A. & Royston, P. (2011). Tutorial in biostatistics: Multiple imputation using chained equations: issues and guidance for practice. Statistics in Medicine 30, 377–399. http://dx.doi.org/10.1002/sim.4067