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Tim Disher, RN, PhD

Senior Director, Biostatistics


Data & Analytics, Health Economic Research, HEOR

Tim is an experienced analyst and emerging thought leader in the application of Bayesian methods to complex problems. His Vanier Canada Graduate Funded dissertation research focused on the  incorporation of multivariate evidence synthesis in health economic models in neonatology. Tim’s expertise includes developing and critiquing models through a lens that blends statistical and clinical considerations. He is a Cochrane author with publications in high impact pediatrics journals and the recipient of the prestigious JAMA Pediatrics trainee publication award. Tim is a PhD candidate in nursing and Vanier Canada Scholar, and holds a BSc in nursing and psychology, both at Dalhousie University.

Articles by Tim Disher, RN, PhD

The AI Revolution in HEOR: How ChatGPT-4o and Multi-AI Agents Are Shaping

Discover the future of Health Economics and Outcomes Research (HEOR) in our article, ‘The AI Revolution in HEOR’. Learn how AI, particularly ChatGPT-4o, is transforming the field with its enhanced speed and capabilities. Explore the potential of multi-AI agents in augmenting HEOR workflows and the emerging role of AI in supporting core HEOR services. This […]

Unveiling Challenges: Analyzing Stakeholder Feedback on the EU Joint Clinical Assessment Implementation Act and Its Impact on the Latest Draft

On March 5, 2024, the draft Implementation Act (IA) for Joint Clinical Assessment (JCA) for medicinal products was released for public comments. The intent of the draft IA was to provide more granular details on the JCA process within the framework of the Health Technology Assessment Regulation (HTAR). By the April 2, 2024, deadline, 129 […]

Transforming Systematic Literature Reviews: Leveraging AI for Efficiency and Rigor

Systematic literature reviews (SLRs) are instrumental in supporting healthcare decision-making and market access. Traditionally, literature reviews require a resource-intensive process, thus highlighting the need for more efficient methodologies without compromising rigor or integrity. Over the past few years, artificial intelligence (AI) has emerged as a powerful tool that can significantly enhance efficiency in conducting reviews. […]

Filling the Missing Spaces in Your Network Meta-Analysis: The Role of Surrogate Models

Network Meta-Analysis Network meta-analysis is a statistical method that combines data from multiple randomized controlled trials to compare the relative effectiveness of different interventions (Dias et al. 2011). This method allows researchers to synthesize the results of trials that have evaluated the same health outcomes, but have used different treatments or interventions. Network meta-analysis provides a […]

ISPOR 2020 PODIUM: Methodological Challenges with NMAs Assessing Long-term Efficacy in Psoriasis

METHODOLOGICAL CHALLENGES WITH CONDUCTING NETWORK META-ANALYSES ASSESSING LONG-TERM COMPARATIVE EFFICACY IN PSORIASIS- A CRITIQUE OF ASSUMPTIONS UNDERPINNING RECENT INDIRECT TREATMENT COMPARISONS Virtual ISPOR 2020 | TUESDAY, May 19th, 2020 | 5:30-5:45 PM ET OBJECTIVES: In network meta-analysis (NMA) of psoriasis trials, cross-over after an initial placebo-controlled period limits the connectivity of long-term evidence networks. We illustrate […]

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