UBC Theses and Dissertations
Advancing the methods and accessibility of cost-effectiveness and value of information analyses in health care Sadatsafavi, Mohsen
This thesis comprises three methodological advancements that address important issues related to cost-effectiveness analysis (CEA) and expected value of information (EVI) analysis in health technology assessment. Aims: 1) To develop a practical sampling scheme for the incorporation of external evidence in CEAs conducted alongside randomized controlled trials (RCT); 2) To develop non-parametric methods for the calculation of the expected value of sample information (EVSI) for RCT-based CEAs; 3) To develop a computationally efficient algorithm for the calculation of single-parameter expected value of partial perfect information (EVPPI) for RCT-based and model-based CEAs. The theories and methods laid out in this work are accompanied by real-world CEA and EVI analyses of the Canadian Optimal Therapy of Chronic Obstructive Pulmonary Diseases (OPTIMAL) trial, a RCT on combination pharmaceutical therapies in chronic obstructive pulmonary diseases (COPD). Results: 1) The ‘vetted bootstrap’ is a semi-parametric algorithm based on rejection sampling and bootstrapping that allows the incorporation of external evidence into RCT-based CEAs. Implementing this method to incorporate external information on the effect size of treatment in the OPTIMAL trial required only minor modifications to the original CEA algorithm. 2) A Bayesian interpretation of the bootstrap allows non-parametric calculation of EVSI through two-level resampling. In the case study, incorporation of missing value imputation and adjustment for covariate imbalance in EVI calculations generated EVSI and the expected value of perfect information (EVPI) values that were significantly different than those calculated conventionally, demonstrating the flexibility of this method and the potential impact of modeling such aspects of the analysis on EVI calculations. 3) The new method enabled the calculation of EVPPI for the effect size of treatment for the exemplary RCT data, and showed a significant (up to 25 times in terms of root-mean-squared error) improvement in efficiency compared to the conventional EVPPI calculation methods in a series of simulations. Summary: This thesis provides several original advancements in the methodology of the CEA and EVI analysis of RCTs and enables several analytical approaches that have hitherto been available only through parametric modeling of RCT data.
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