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This thesis addresses the issue of incorporating real world data (RWD) on treatment effectiveness into the health technology assessment (HTA) process. Evidence synthesis methods are evaluated, developed, and illustrated using a case-study assessing the effectiveness of biologic drugs as treatments for rheumatoid arthritis (RA). An extensive simulation study is undertaken comparing existing methods, under a Bayesian framework, for performing a pairwise meta-analysis combining randomised controlled trials (RCTs) and single-arm trials (SATs) using aggregate triallevel data (AD). The simulation study results suggest that prior-based methods provide a  exible and robust approach to incorporate SAT data into a meta-analysis, by defining an informative prior distribution for the pooled treatment effect which can be down-weighted according to pre-specification or commensurability with the RCT data. Novel methods are developed for performing a network meta-analysis combining RCTs and SATs using a mixture of individual participant data and AD, with covariate adjustment on the baseline response. The application of the developed methods to data from a network of RCTs, assessing a number of biologic drugs versus placebo as treatments for RA, suggests that the predicted baseline response for SATs can significantly in uence the pooled treatment effect estimates. The impact of methods for incorporating RWD (including disease-registry data) is demonstrated by repeating the cost-effectiveness analysis from a previous HTA report, using the updated clinical effectiveness estimates. The incremental cost-effectiveness ratio estimates, comparing biologic versus non-biologic treatment strategies for RA, were higher when RWD were included and treatment effect estimates based on registry data were lower compared to those based on RCTs alone. RWD can provide information regarding the potential variability in cost-effectiveness results, and should be incorporated into the evidence synthesis to improve HTA decision-making.

Within the drug development process, drugs are first tested in preclinical trials consisting of animal and/or in vitro studies, followed by several phases of clinical trials in humans. For a new drug to be licensed it must pass through regulatory approval and, for it to be reimbursed by a national healthcare provider such as the UK NHS, be assessed in a Health Technology Appraisal (HTA). Treatments may subsequently be evaluated and recommended as part of national or international guidelines. Synthesis of evidence is performed at different points throughout this process, but often in relative isolation and using different methodologies. Furthermore, available evidence can be sparse, which poses challenges for robust synthesis, particularly if such analyses are intended to inform decision-making. This thesis aims to characterise the use of evidence synthesis within drug development and reimbursement, and to explore which sources of data may be useful at different points for decision-making by addressing the following questions: 1. What are the barriers to using evidence synthesis to support translation of research findings from preclinical studies to human trials? 2. What robust methods can we use to incorporate dose-response and time-course information into evidence synthesis? 3. Can we use early phase evidence to connect evidence networks in reimbursement decision-making? 4. Which tools may be helpful for evidence synthesis of drug development trials? The thesis includes published papers and software to explore these questions, including developing and evaluating a framework for explicitly modelling dose-response and time-course relationships using Model-Based Network Meta-Analysis (MBNMA). The integrative chapter places these developments in the context of other work, discusses strengths and limitations of proposed approaches, and suggests future research that could build upon the work in the thesis.

Abstract: Rising healthcare cost and improving the quality of care delivered by Alberta’s healthcare system has long been a part of popular discourse. This need to reduce healthcare spending while still maintaining or improving pre-existing quality of care has led many to look towards incorporating new health technology. However, before we can deploy innovative health technologies into the healthcare system, an evaluation of its pros, cons, capabilities, usability, and amongst factors is done. This assessment, or more formally known as a health technology assessment, is where a committee collects evidence and research about a health technology to be presented to healthcare authorities and decision makers. One attribute that is less studied in health technology assessments compared to other elements such as economic evaluation is user adoption and acceptance of technology. Research into the methodologies and reasoning’s behind individual-level adoption of technologies is one of the most mature streams of research within information system (IS). When expanding the scope of current health technology assessments to understand the adoption of technology from an organization-level or group-level exposes new facets for analysis that explores what influences employees’ productivity, output, and job performance. Over the years, different theoretical models have been developed to describe technology adoption such as the Technology Acceptance Model (TAM and TAM2) (Davis, 1985; Venkatesh & Davis, 2000). In 2003, Venkatesh, Morris, Davis, and Davis proposed a synthesis of these technology adoption models, the Unified Theory of Acceptance and Use of Technology (UTAUT). This research proposes that UTAUT can be used as a framework to be used in health technology assessment in Alberta Health Services. This translation is done to showcase how users adopt and accept new health technology, challenges they face, potential solutions, and demonstrating that UTAUT is a worthwhile, effective, and simple framework in analyzing user adoption and acceptance of new health technology.