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Title: The cost-effectiveness of novel biomarkers for the prevention of cardiovascular disease
Author: Lamrock, Felicity
ISNI:       0000 0004 6424 6261
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2017
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Cardiovascular disease is still highly prevalent worldwide, and it has been shown that novel biomarkers such as C-reactive protein have the potential ability to predict who is at risk for a cardiovascular event. Decision-analytic models can be used to assess whether different prevention strategies are not only effective but cost-effective. Within decision-analytic models, Markov models have often been used to quantify the movements of individuals between different health states over time, where movements can be influenced by characteristics of the individuals as well as the prevention strategies being applied. This thesis presents a new five-state Markov model to capture the flow of individuals across the different health states. Hazard ratios for conventional risk factors, as well as several novel biomarkers, are obtained for each of the permitted transitions between health states. Several approaches are used to obtain the hazard ratios, and a novel biomarker panel score created by linearly combining three novel biomarkers: C-reactive protein, NT-pro BNP, and Troponin I. Net reclassification indices are calculated to quantify the movements between risk categories as defined by European guidelines, with and without the use of one or more novel biomarkers for 10 year risk prediction of cardiovascular death. Transition probabilities between each of the health states are calculated for a number of different strategies, and combined with cost and utility information to create a cost-effectiveness model. Individuals deemed to be at intermediate risk of a cardiovascular event are assessed to address if the use of the novel biomarker panel score is cost-effective. A sensitivity analysis is performed to assess the robustness of the cost-effectiveness model by varying parameter inputs and performing a deterministic and probabilistic sensitivity analysis. A validation of the model is also performed to assess how closely the model predicts the number of deaths compared to those that occurred.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available