This dissertation presents a comparison of modelling techniques for evaluating healthcare interventions with a focus on modelling coronary heart disease interventions. Through the construction of decision tree, Markov and discrete event stimulation (DES) models for simple hypothetical and realistic healthcare models, the dissertation compares the respective processes and outputs of the alternative techniques. The results are analysed and recommendations are made for theoretical guidelines for the choice of modelling technique according to various intervention classifications. This research is the first to compare the modelling techniques from an empirical perspective for several intervention types and to provide a serious comparison of the benefits or disadvantages of the modelling approaches. In addition the models for coronary heart disease provide realistic assessment of the benefits and costs of improved emergency response times, secondary prevention medication and bypass surgery. The coronary heart disease models are based upon research completed by the author as part of the UK Coronary Heart Disease Policy Model working team. The interventions are shown to be good value for money according to a willingness to pay threshold of £30,000 per QALY gained. Aspirin and beta blockers are the most cost effective and have incremental cost effectiveness ratios (ICER) of less than £1000 per QALY. Improving thrombolysis response is the last cost effective with an ICER of almost £30,000 per QALY gained. In order to achieve the targets from the National Service Framework (NSF), the increased spending (and consequent health benefits) would be greatest for statins and revascularisation. Implementing each of the NSF scenarios for England over the next 20 years for these interventions would result in an average annual extra cost of £400 million and will result in a saving of 65,000 life years and 70,000 QALYs each year.