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ObjectiveThe Canadian Real-world Evidence for Value in Cancer (CanREValue) Collaboration was established in response to growing interest in using real-world evidence (RWE) to support health technology assessment (HTA). CanREValue has developed a framework to generate and use RWE to inform cancer drug funding decisions.Design and participantsThe RWE framework was developed using a multistage, multistakeholder approach. First, an environmental scan and qualitative study were conducted to understand the current state and key stakeholder perspectives on RWE. Next, five formal working groups (WGs) were established consisting of stakeholders with cancer drug funding expertise including clinicians, patients, methodologists, payers, regulatory decision-makers and data analysts. Through stakeholder consultations, including modified Delphi exercises and workshops, each WG developed specific framework components and identified facilitators and barriers that may impact the uptake of RWE.SettingThe CanREValue Collaboration consisted of membership and participation from stakeholders and expertise from across Canada. Central research operations were managed from Toronto, Ontario, Canada.OutcomesDevelopment of an RWE framework reflective of the needs and perspectives of stakeholders directly involved and/or impacted by cancer drug funding decisions across Canada.ResultsThrough an iterative process, a comprehensive RWE framework was developed that outlined the end-to-end processes necessary for the generation and use of RWE for HTA reassessment in Canada. The framework consists of four phases that uses various tools, templates and processes, which can be applied as a whole or in part. A diverse range of stakeholders and expertise is involved in the decision-making of each phase of the process: Phase I: identification, selection and prioritisation of RWE questions; phase II: initiating and planning the RWE study; phase III: conducting the RWE study and phase IV: conducting reassessment.ConclusionsAs the cancer drug funding landscape continues to evolve, the need for RWE to support evidence-based policy reform, pricing and reallocation of funding from low to high value settings is crucial. We have developed a framework that is adaptable and responsive to the changing landscape. The tools, templates and processes within the framework can be applied by various stakeholder groups in whole or in part to support cancer drug funding decision-making in Canada and can be adapted for use in other jurisdictions.

BackgroundEconomic analyses based on clinical trial data are costly and time consuming, and alternative methods for performing economic analyses should be explored.Objective and methodsIn this perspective, we examine the emerging role of administrative data for economic analyses in cancer.ResultsCompared with routinely collected clinical trial data, routinely collected administrative data have several strengths including high capture rates for healthcare encounters, less resource utilisation, low rates of misclassification, long follow-up periods and the opportunity to collect data points not traditionally captured in clinical trials. However, there are also limitations including the need for accurate data linkage across multiple databases and systems, the costs and time associated with data linkage, the potential time lag between trial data collection and the availability of administrative data, and limited data on quality of life, toxicity and indirect costs. In this perspective, we identify important barriers and potential solutions to performing economic analyses for oncology using administrative data, and outline strategies to increase research in this field.ConclusionThe use of routinely collected administrative data sets for economic analyses of clinical trials presents a unique opportunity that could complement and validate economic analyses based on trial-level data.

Uncertainties in health utilities have received little attention in the literature. Health utility is a preference-based quality of life measure. It is commonly used to calculate quality-adjusted life years and to conduct cost-utility analysis. Under-estimation of the uncertainty of health utilities will lead to under-estimation of the uncertainty of the results of any economic evaluation that requires the use of health utilities. This thesis focuses on addressing uncertainty in health utilities in three areas: (1) the underestimation of uncertainties of health utilities derived from mapping algorithms and the derivation of methods to address it, (2) the challenge with prediction error in multi-attribute utility instrument valuation studies with small sample size and an emphasis of the use of shrinkage estimators as a potential solution, and (3) the underestimation of uncertainties of health utilities derived from multi-attribute utility instruments with an emphasis of the use of multiple imputation as a potential solution. For each issue, theoretical statistical explanations are provided and then followed by the proposed methods to address those issues. Simulation methods are also used to explore the usefulness of the proposed methods.