Missing data

Missing data is a common problem in clinical trials and we have explored standard and new methods to deal with incomplete datasets proposed in the statistical literature. In addition, we have shown how these methods can contribute to the appropriate analysis of economic data. Published trial-based economic evaluations which use these techniques are noted below.

The performance of multiple imputation software to analyse semi-continuous data has been evaluated by Oliver Rivero-Arias in collaboration with Ly-Mee Yu (Centre for Statistics in Medicine, Oxford) and Andrea Burton (Warwick Clinical Trials Unit, University of Warwick), and results published in Statistical Methods for Medical Research in 2007.

Publications

Yu, L, Burton, A and Rivero-Arias, O (2007). Evaluation of software for multiple imputation of semi-continuous data. Statistical Methods in Medical Research(16):243-258.

Applied research using missing data imputation methods

Rivero-Arias, O, Gray, A, Frost, H, Lamb, SE, and Stewart-Brown, S (2006). Cost-utility analysis of physiotherapy treatment compared with physiotherapy advice in low back pain. Spine 31(12):1381-7

Rivero-Arias O, Campbell H, Gray A, Fairbank J, Frost H, Wilson-MacDonald J (2005). Surgical stabilisation of the spine compared with a programme of intensive rehabilitation for the management of patients with chronic low back pain: cost-utility analysis based on a randomised controlled trial. British Medical Journal 330 (7502):1239-1243.