2014; Gomes et al. 2012); which led to 70 imputations being used. Costs and change in QALYs from baseline were jointly imputed using patient age, and parity and treatment group as explanatory imputation variables. The imputation was performed by treatment group for all imputed variables except baseline utility. Imputation of baseline utility was performed across all observations rather than specified by treatment group. The MI process generates the imputed data by using the Multiple Imputation Chained Equation (MICE) with Predictive Mean Matching (PMM) method (Faria et al. 2014). This model generated imputed values by prediction of missing data from the posterior distribution of missing observations given the values of observed data from the imputation variables. The SUR was then undertaken across the 70 resultant datasets in order to take account of the uncertainty in the imputed values, but in a way that recognises that the datasets do not represent independent observations. Likewise, the cost-effectiveness confidence ellipses and cost-effectiveness acceptability curves (CEAC) were generated (parametrically) using means, correlations and standard errors generated from the output of the SUR estimated across the 70 datasets (Hoch et al. 2002). Five parameters were used in this analysis: the difference in mean total cost, standard error of the difference in mean total cost, difference in mean QALYs, standard error of the difference in mean QALYs and the covariance between cost and QALYs. These parameters were used for producing the CEAC and confidence ellipses.
2014; Gomes et al. 2012); which led to 70 imputations being used. Costs and change in QALYs from baseline were jointly imputed using patient age, and parity and treatment group as explanatory imputation variables. The imputation was performed by treatment group for all imputed variables except baseline utility. Imputation of baseline utility was performed across all observations rather than specified by treatment group. The MI process generates the imputed data by using the Multiple Imputation Chained Equation (MICE) with Predictive Mean Matching (PMM) method (Faria et al. 2014). This model generated imputed values by prediction of missing data from the posterior distribution of missing observations given the values of observed data from the imputation variables. The SUR was then undertaken across the 70 resultant datasets in order to take account of the uncertainty in the imputed values, but in a way that recognises that the datasets do not represent independent observations. Likewise, the cost-effectiveness confidence ellipses and cost-effectiveness acceptability curves (CEAC) were generated (parametrically) using means, correlations and standard errors generated from the output of the SUR estimated across the 70 datasets (Hoch et al. 2002). Five parameters were used in this analysis: the difference in mean total cost, standard error of the difference in mean total cost, difference in mean QALYs, standard error of the difference in mean QALYs and the covariance between cost and QALYs. These parameters were used for producing the CEAC and confidence ellipses.
VKBrennan%20THESIS%20FINAL%20%28including%20appendix%29.docx