D

Diaz-ordaz, K, Kenward, M, Gomes, M and Grieve, R (2016). Multiple imputation methods for bivariate outcomes in cluster randomised trials: Supporting Information. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.99.

L

Leurent, B, Gomes, M and Carpenter, J (2017). Missing data in trial-based cost-effectiveness analysis review dataset. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.272.

Leurent, B, Gomes, M, Faria, R, Morris, S, Grieve, R and Carpenter, JR (2018). Sensitivity analysis for missing data in cost-effectiveness analysis: Stata code. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.6714206.v1

M

Morris, TP, Walker, AS, Williamson, EJ and White, IR (2022). Additional file 1 of Planning a method for covariate adjustment in individually randomised trials: a practical guide. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.19613608.v1

Morris, TP, Walker, AS, Williamson, EJ and White, IR (2022). Additional file 2 of Planning a method for covariate adjustment in individually randomised trials: a practical guide. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.19613611.v1

Mtenga, B, Beard, J, Todd, J and Urassa, M (2021). Kisesa Tanzania - HIV missing data analysis. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00002083.

T

Tackney, MS, Cook, DG, Stahl, D, Ismail, K, Williamson, E and Carpenter, J (2021). Additional file 1 of A framework for handling missing accelerometer outcome data in trials. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.14738652.v1