191 17 1832 2 6247 document 1832 Data_Generating_Function.R text/plain a228ef4bfe878e7f4ed1a652314ed08d0a4e522cb700b97f7542f311005e8a92 SHA256 13302 2016-03-23 11:57:28 https://datacompass.lshtm.ac.uk/id/eprint/191/1/Data_Generating_Function.R 191 1 1 text/plain Software en public cc_by

Data_Generating_Function.R

study_instrument indefinite R code created to model influencing factors that affect the handling of missing data archive 3 disk0/00/00/01/91 2016-03-23 12:01:22 2021-07-09 11:22:03 2016-03-23 12:01:22 data_collection show Diaz-ordaz Karla 0000-0003-3155-1561 FALSE c3078c6b45cf2b23c1efa331f581a993 Kenward Mike FALSE 89c4f3807d3e295c295d369a64b5cf57 Gomes Manuel FALSE 3b7a5f3ff3c9e3a7c6f83c3d704d2bf5 Grieve Richard 0000-0001-8899-1301 FALSE 9ffeb7ca857f2b93c9bab8be31fa02e4 London School of Hygiene & Tropical Medicine EPMS PHSR Missing data Clinical trials Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost-effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approaches on bivariate continuous outcomes, in terms of confidence interval coverage and empirical bias in the estimated treatment effects. Missing-at-random clustered data scenarios were simulated following a full-factorial design. 2016-03-14 published London School of Hygiene & Tropical Medicine 10.17037/DATA.99 Dataset London School of Hygiene & Tropical Medicine Simulation public London, United Kingdom Medical Research Council NHS National Institute for Health Research Dataset Quantitative en http://researchonline.lshtm.ac.uk/id/eprint/2535612 Medical Research Council NHS National Institute for Health Research 2016-03-14