Decision-analytic models are the preferred method by the National Institute for Health and Care Excellence for generating evidence on the cost-effectiveness of competing interventions. Increasingly patients are experiencing multiple conditions simultaneously which poses key challenges to this model-based approach. The aim of this thesis was to contribute towards methods for incorporating multiple conditions within decision-analytic models for economic evaluation. Large administrative and survey datasets were used including NHS Patient Reported Outcome Measures, GP Patient Survey and Health Survey for England to explore the relationship between multiple conditions and health-related quality of life (HRQoL). Regression methods were used to quantify the relationship between increasing numbers of conditions and HRQoL. The analysis explored whether co-occurrence of certain conditions, in particular depression, with physical health conditions lead to larger ('synergistic') reductions in HRQoL than would otherwise be expected. Competing methods (including the additive, multiplicative, minimum, linear index and adjusted decrement estimator) for predicting utility values for populations with multiple conditions were evaluated within the GP Patient Survey data. The linear index, which is a parametric estimator, was externally validated in Health Survey for England data. Conceptual challenges associated with modelling the cost-effectiveness of treating patients with multiple conditions were explored in two separate decision-analytic models. A cohort-based state transition model assessed the cost-effectiveness of statin therapy for the primary prevention of cardiovascular disease when patients had an ongoing direct treatment disutility associated with statin therapy. A patient-level model incorporated condition-interactions by assessing the cost-effectiveness of ten antidepressants for patients with depression at risk of cardiovascular disease. A synergistic relationship was observed between depression and the number of reported physical health conditions leading to large falls in HRQoL as patients become increasingly multimorbid. When predicting utility values for patients with multiple conditions, the multiplicative method and linear index performed best with lowest bias and highest precision but both were dependent on key assumptions regarding the inclusion of co-morbidities and the selection of a healthy baseline. Estimates for all methods were biased in the presence of depression, over-estimating predicted utility values compared to true utility values. The linear index was robust in the external validation when the same assumptions were used in the validation cohort as in the derivation cohort. Overall, including multiple conditions within decision-analytic models for economic evaluation was found to be feasible but challenging. The development of methods within this thesis will allow future analysts to estimate the HRQoL for patient populations with multiple conditions where no such data currently exists.