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ELEVATE-GenAI: A New Guideline for Reporting Generative AI in HEOR Workflows

Generative artificial intelligence (AI), particularly large language models (LLMs), is increasingly embedded in health economics and outcomes research (HEOR) workflows. Researchers are now using these tools to support activities such as systematic literature reviews, health economic modeling, and real-world evidence generation.

As adoption grows, so does a fundamental question for the HEOR community:

How should the use of generative AI be transparently and consistently reported within HEOR workflows?

To address this question, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Working Group on AI has developed ELEVATE-GenAI — a reporting guideline specifically designed to document and communicate how generative AI is used in HEOR research.

Why a dedicated reporting guideline is needed

HEOR has a strong tradition of structured reporting, supported by well-established standards for systematic reviews, economic evaluations, and real-world evidence. However, the rapid integration of LLMs into HEOR workflows has outpaced the development of HEOR-specific guidance on how their use should be reported.

LLMs are now being applied to:

Screening and classifying abstracts in systematic literature reviews
Extracting data and assessing bias
Building or replicating health economic models
Transforming unstructured real-world data into analyzable formats

While these applications offer efficiency and scalability, they also introduce new challenges related to transparency, reproducibility, factual accuracy, bias, uncertainty, and data governance. Existing AI reporting guidelines do not fully address these challenges in the context of HEOR decision-making, regulatory review, or health technology assessment (HTA).

ELEVATE-GenAI was developed to fill this gap by providing clear, HEOR-specific guidance for reporting the use of generative AI within research workflows.

What is ELEVATE-GenAI?

ELEVATE-GenAI is a reporting framework and checklist intended for HEOR studies in which generative AI plays a substantive role in evidence generation, synthesis, or analysis. Its goal is not to evaluate the performance of specific AI tools or to prescribe how AI should be used, but rather to ensure that AI-assisted workflows are clearly described, interpretable, and reproducible.

The guideline is designed to support:

Authors, by clarifying what information should be reported
Reviewers and editors, by enabling consistent evaluation
HTA bodies and regulators, by improving transparency and trust

Importantly, ELEVATE-GenAI is not intended for studies that use AI only for minor tasks such as editing or formatting text. Instead, it applies when generative AI meaningfully influences HEOR outputs.

Reporting generative AI across HEOR workflows: The 10 ELEVATE domains

At the center of ELEVATE-GenAI is a set of 10 reporting domains that together describe how generative AI is integrated into HEOR workflows and how its outputs are assessed.

1. Model characteristics

This domain ensures clarity about what AI system was used. Authors are encouraged to report the model name and version, developer, access method, license type, architecture, and — where available — training and fine-tuning data sources.

2. Accuracy assessment

Accuracy reporting focuses on how closely AI-generated outputs align with expected or correct results, using task-appropriate benchmarks such as expert review, gold-standard datasets, or quantitative performance measures.

3. Comprehensiveness assessment

Comprehensiveness addresses whether AI outputs fully cover all relevant elements of a task — for example, whether all key studies were captured in a literature review or all required components were included in an economic model.

4. Factuality verification

This domain emphasizes verification of factual correctness, including identifying and correcting hallucinated citations, incorrect data, or unsupported claims generated by the model.

5. Reproducibility and generalizability

Authors are encouraged to document prompts, parameters, workflows, and model versions to support reproducibility, and to discuss whether the AI-assisted approach can be applied to similar HEOR questions or settings.

6. Robustness checks

Robustness reporting addresses how sensitive AI outputs are to changes in inputs, such as minor prompt variations, ambiguous wording, or typographical errors.

7. Fairness and bias monitoring

Where applicable, studies should assess whether AI outputs introduce or reinforce biases related to demographic or population characteristics relevant to HEOR analyses.

8. Deployment context and efficiency

This domain captures practical aspects of AI deployment, including hardware and software configurations, processing time, scalability, and resource requirements — factors that influence real-world feasibility.

9. Calibration and uncertainty

Calibration focuses on whether AI confidence aligns with actual performance and how uncertainty is handled, such as defining thresholds for human review in hybrid AI–human workflows.

10. Security and privacy measures

Authors should describe how sensitive data, intellectual property, and regulatory requirements (e.g., GDPR or HIPAA) are addressed when generative AI is used in HEOR workflows.

Each domain is accompanied by reporting guidance and an assessment of metric maturity, recognizing that some areas — such as fairness and uncertainty — are still evolving.

From framework to practice: The ELEVATE checklist

To facilitate adoption, ELEVATE-GenAI includes a practical checklist that translates the 10 domains into concrete reporting questions. An optional scoring system allows authors and reviewers to summarize reporting completeness, while emphasizing that this score is not a measure of methodological quality or study validity.

The authors demonstrate the applicability of the guideline by retrospectively applying it to two published HEOR studies — one focused on systematic literature review automation and another on health economic modeling. These examples show how ELEVATE-GenAI can be used to consistently describe AI-assisted workflows across different HEOR applications and to identify areas where reporting can be strengthened.

Why ELEVATE-GenAI matters for HEOR

As generative AI becomes more deeply integrated into HEOR workflows, transparent reporting is essential to maintain scientific credibility and stakeholder trust. ELEVATE-GenAI provides a shared structure for documenting how AI is used, how outputs are evaluated, and what limitations may affect interpretation.

By establishing common expectations for reporting generative AI in HEOR, ELEVATE-GenAI supports responsible innovation while aligning with the needs of journals, HTA bodies, and regulators.

Final takeaways

ELEVATE-GenAI positions itself as a foundational guideline for reporting the use of generative AI in HEOR workflows. By focusing on transparency, reproducibility, and interpretability, it helps ensure that AI-augmented research can be critically assessed and confidently used in healthcare decision-making.

As a living guideline, ELEVATE-GenAI will continue to evolve alongside advances in generative AI — providing the HEOR community with a practical framework for integrating new technologies without compromising rigor or trust.

Interested in learning more?

Read the full paper: “ELEVATE-GenAI: Reporting Guidelines for the Use of Large Language Models in Health Economics and Outcomes Research: An ISPOR Working Group Report.”

From Regulators to Reimbursement: What the EMA-FDA AI Principles Mean for HEOR

In January 2026, the European Medicines Agency (EMA), together with the U.S. Food and Drug Administration (FDA), have taken an important step by publishing the “Guiding Principles of Good AI Practice in Drug Development.” This document is more than a technical checklist — it is a clear signal that regulators are getting serious about how artificial intelligence (AI) should be developed, validated, governed, and, ultimately, trusted across the medicines lifecycle.

While the principles are formally framed around drug development, their implications go well beyond non-clinical and clinical domains. For Health Economics and Outcomes Research (HEOR), this guidance offers something the field has long needed: a credible regulatory blueprint for responsible AI use that could help agencies move from cautious experimentation to structured adoption.

Why this matters now

AI is already being used across HEOR — whether for real-world evidence generation, economic modeling, patient segmentation, or long-term outcome prediction. Yet, despite methodological innovation, acceptance by HTA bodies and payers remains uneven. One of the key barriers is not capability, but confidence: confidence in transparency, robustness, reproducibility, and governance.

By articulating shared principles for AI use, the EMA and its partners are laying the groundwork for that confidence. Importantly, they are doing so in a way that aligns closely with the questions HTA agencies ask every day: What is this model for? What risks does it introduce? Can we trust the outputs? And how do we manage it over time?

A bridge to HEOR: Learning from regulatory leadership

We have already seen how regulatory clarity can accelerate adoption. The UK, for example, has actively explored how AI can be used to support evidence generation and decision-making in health systems. EMA-FDA’s principles create an opportunity to extend this momentum across Europe and beyond — including into HEOR and HTA decision frameworks.

Although all ten principles are relevant, four stand out as particularly transformative for HEOR.

Four principles with outsized impact on HEOR

1. Human-centric by design

This principle explicitly anchors AI development in ethical and human-centric values. For HEOR, this is critical. Economic models and real-world analyses directly influence access, reimbursement, and, ultimately, patient outcomes.

A human-centric approach reinforces that AI in HEOR should support, not replace, expert judgement. It legitimizes hybrid workflows where analysts, clinicians, patients, and decision-makers remain central, while AI enhances scale, speed, and insight. This framing directly addresses common HTA concerns about “black box” decision-making.

2. Risk-based approach

Not all AI use cases carry the same consequences, and this principle explicitly recognizes this. For HEOR, this principle is particularly powerful.

Using AI to automate literature screening does not pose the same risk as using it to inform long-term survival extrapolations or pricing decisions. A risk-based approach allows proportionate validation, governance, and oversight — making AI adoption more realistic and scalable for both developers and agencies.

This is precisely the kind of nuance HTA bodies need to move beyond binary “acceptable/not acceptable” positions on AI.

3. Risk-based performance assessment

Closely linked, the EMA and FDA emphasize that performance assessment should consider the complete system, including human-AI interaction, and be tailored to the intended context of use.

For HEOR, this reframes validation away from abstract accuracy metrics and toward decision relevance. The key question becomes: Is this AI fit-for-purpose for the policy or reimbursement decision it supports? This aligns naturally with HTA thinking and opens the door to more pragmatic, decision-focused validation frameworks.

4. Life cycle management

Perhaps the most underappreciated principle in HEOR today is life cycle management. The EMA highlights the need for ongoing monitoring, re-evaluation, and management of issues such as data drift.

HEOR models are often treated as static artefacts, yet AI-enabled models evolve as data, clinical practice, and populations change. Recognizing AI as a living system — not a one-off submission — could fundamentally change how HTA agencies think about post-submission evidence generation, managed entry agreements, and reassessment over time.

From drug development to HTA: An opportunity not to miss

This guidance is explicitly focused on drug development, but its principles are intentionally broad and collaborative. They invite extension, adaptation, and harmonization across jurisdictions and evidence domains.

For HEOR, this is an opportunity. By aligning AI methods with regulatory expectations early — rather than waiting for explicit HTA-specific rules — the field can help shape how agencies evaluate AI-enabled evidence. In doing so, HEOR can move from being a passive recipient of regulation to an active contributor to responsible AI adoption.

Looking ahead

AI will not replace HEOR expertise — but it will increasingly shape how evidence is generated, synthesized, and interpreted. These guiding principles offer a shared language to discuss trust, risk, and value. If agencies apply similar thinking to HEOR, we may finally see a path toward consistent, transparent, and confident use of AI in reimbursement and access decisions.

In that sense, this guidance is not just about AI in drug development. It is about preparing the entire evidence ecosystem — including HEOR — for a future where intelligent systems are used responsibly, transparently, and in service of better patient outcomes.

Interested in learning more?

Watch our recent webinar, “AI in HEOR: Case Studies on Navigating Regulatory and HTA Guidance,” on demand, featuring experts Dalia Dawoud, Manuel Cossio, Sheena Singh, and Cale Harrison:

Advancing Equity in Health Technology Assessment: Lessons from CAR T-Cell Therapies

Chimeric antigen receptor (CAR) T-cell therapies, classified as advanced therapy medicine products, have revolutionized the treatment landscape for certain hematological cancers, providing new hope to patients who previously had limited options. Since the U.S. FDA approved tisagenlecleucel (Kymriah) and axicabtagene ciloleucel (Yescarta) in 2017 for relapsed or refractory B-cell precursor acute lymphoblastic leukemia and large B-cell lymphoma, respectively, evidence has suggested that CAR T-cell therapies could offer a potentially curative approach in a range of other hematological conditions.1,2,3,4

However, despite their potential to improve patient outcomes, access to CAR T-cell therapies remains inconsistent due to cost, delivery complexity, and manufacturing challenges. Additionally, disparities in access related to social determinants of health (SDOH) further limit equitable benefits, disproportionately impacting marginalized populations (such as those living in rural areas, individuals with no family or social networks, and older people).

Health technology assessment (HTA) has traditionally focused on clinical outcomes and cost-effectiveness. Although health equity has been recognized as a distinct value element in HTA, and relevant frameworks and guidelines exist, it is not routinely integrated into decision-making. As such, CAR T-cell therapies represent a valuable case study for better understanding and advancing equity considerations in HTA.

What are CAR T-cell therapies?

CAR T-cell therapies are a type of immunotherapy that modify a patient’s T-cells to target and attack cancer cells, offering effective options for relapsed or refractory hematological cancers. This process involves extracting, modifying, and reinfusing the cells, followed by close monitoring for severe adverse events. Beyond their current approved indications, CAR T-cell therapies are also being investigated for several other hematological malignancies, as well as in solid tumors and non-cancer indications such as autoimmune conditions.4

Delivering CAR T-cell therapies presents significant challenges for healthcare systems due to their complexity, high cost, and the need for specialized infrastructure and expertise. The treatment requires apheresis, cell manufacturing, conditioning therapy, and intensive post-infusion monitoring, all conducted at accredited centers, often located in major urban areas.5 Successful delivery also requires coordination among a multidisciplinary team of physicians, nurses, and pharmacists, along with investment in treatment center infrastructure, including intensive care unit capacity and specialized training to manage severe adverse events (e.g., cytokine release syndrome and neurotoxicity).5

CAR T-cell therapies: Highlighting equity concerns in access to innovative treatments

Ensuring that equitable access to healthcare is considered in the HTA decision-making, particularly for high-cost, innovative treatments like CAR T-cell therapy, has become a growing concern. Despite advancements in science, therapeutic applications, and complication management, access to CAR T-cell therapy remains limited, with only a small percentage of eligible patients receiving treatment.6,7 This restricted access stems from challenges specific to CAR T-cell therapy, such as high costs, complex logistics, and manufacturing constraints, which are compounded by factors related to SDOH and equity.

Equity gaps are evident in disease incidence and prevalence, treatment patterns, and outcomes of patients eligible for CAR T-cell therapies. For example, racial and ethnic minorities, particularly Black and Hispanic populations, experience higher rates of certain hematological malignancies, yet are underrepresented in clinical trials that inform CAR T-cell therapy approvals.8,9 This leads to gaps in effectiveness and safety data across populations. Furthermore, differences in diagnosis and referral patterns contribute to inequities, with marginalized groups less likely to be referred to specialized centers due to limited provider awareness or implicit biases. Older adults, who could benefit from CAR T-cell therapies, are often excluded from trials, limiting evidence for their use in this population.10 SDOH, such as geographic remoteness and socioeconomic status, exacerbate inequities in access to CAR T-cell therapies once they are approved. Patients living in rural areas face logistical and financial barriers to reaching treatment centers, while individuals from lower socioeconomic backgrounds struggle with transportation, caregiving responsibilities, and lost wages.11,12 These overlapping disparities create a cumulative burden, limiting equitable access and worsening outcomes for historically underserved groups.

Exploring equity factors in HTAs of CAR T-cell therapies and the journey toward inclusive access

Traditional HTA frameworks have historically overlooked equity considerations, prioritizing clinical efficacy and cost-effectiveness while neglecting how SDOH and equity factors affect patient access and outcomes. This gap not only exacerbates disparities but also fails to incentivize health technology developers to commit to systematic evidence gathering and addressing these issues in their evidence submissions. While several modified economic modeling approaches that account for equity considerations exist (e.g., distributional cost-effectiveness analyses, equity-based weighting, multi-criteria decision analysis), there is a lack of consensus on which approach is best and how these methods can systematically be incorporated into HTA.13,14 As a result, HTAs often do not account for the unique burdens faced by underserved populations, such as indirect costs related to travel, caregiving, and lost income, further exacerbating existing inequities.

Recent commitments to equity from HTA bodies present valuable opportunities to ensure fair access to novel, high-cost therapies.15,16 CAR T-cell therapies, with their complex delivery and high cost, serve as a compelling case study for examining how HTA bodies incorporate equity considerations into their assessments. To explore this further, we conducted a review of 18 HTAs from Canada’s Drug Agency and the National Institute for Health and Care Excellence, focusing on six CAR T-cell therapies. Our review found that most submissions acknowledged disparities in disease incidence, treatment, and outcomes based on race, socioeconomic status, diagnosis and referral patterns, and age. These disparities were often linked to financial and geographical barriers that disproportionately affect marginalized groups. However, there were limited and inconsistent efforts to quantify these factors in the economic modeling or in the analysis of the clinical evidence submitted. This likely reflects the fact that HTA bodies do not routinely require sponsors to quantify equity concerns within their submissions, leading both decision-makers and companies to potentially overlook these issues.

Cytel will present the results of this review at the 2025 ISPOR conference in Montreal, Canada, where we will explore how gaps in HTA evaluations can inadvertently perpetuate inequities in access to CAR T-cell therapies. Join us at our podium session to learn more about how incorporating equity considerations into HTA processes can promote more equitable outcomes and ensure that all patients, regardless of their background, can benefit from CAR T-cell therapies. Do not miss this opportunity to engage in the discussion on advancing inclusive access to high-cost, innovative therapies.

Addressing equity concerns in CAR T-cell therapies: Strategies for inclusive access

Cytel can support pharmaceutical clients in addressing equity concerns through the following offerings:

Innovative trial designs that consider elements of health equity
Generation of real-world evidence to supplement trial programs
Lifecycle evidence generation to support value in diverse groups of patients
Advanced analytics, such as transportability analyses, to maximize the use of evidence generated in other settings
Quantifying the impact of inequalities in the value proposition of new health technologies.

Career Perspectives: A Conversation with Victor Laliman-Khara

In this latest edition of our Career Perspectives series, we had the pleasure of speaking with Victor Laliman-Khara, Research Principal at Cytel. Victor shares his journey from a background in biostatistics and health economics to a career focused on comparative effectiveness within health research. In this interview, he shares his passion for data-driven decision-making in healthcare, discusses the evolving landscape of analytical methodologies, and reflects on how flexibility in work has shaped both his professional and personal life.

Can you give us a little background on your career so far? What inspired you to pursue a degree as a statistical engineer and how did that lead to a career as a research analyst in health economics?

I am originally from France, where I earned a degree in biostatistics and health economics. Before that, I completed prépas, an intense two-year program focused on mathematics, physics, and robotics, where I discovered my keen interest in statistics. This led me to join ENSAI, a French school specializing in statistics.

During my studies, I had the opportunity to interview with a pharmaceutical company, where I was first introduced to the world of clinical trials and their real-world applications. I became incredibly passionate about the field — seeing how the design of an experiment could lead to groundbreaking treatments that truly change patients’ lives. However, I also recognized the critical importance of integrity and scientific rigor in ensuring we make the right decisions.

In my final year at ENSAI, I realized that health economics was the perfect fit for me. It allowed me to work closely on clinical trials, focusing on understanding patient populations while also exploring market dynamics through indirect treatment comparisons (ITCs). I found it incredibly fulfilling to not only meet efficacy thresholds but also to define a treatment’s place in the market, identify target prescription populations, and guide clients through the rigorous HTA process.

I spent five years in consulting, moving from France to London and then to Toronto in 2016 — a city that was rapidly growing in innovation and technology. From there, I transitioned to pharma, joining Roche for three years to gain firsthand experience in designing clinical trials, particularly adaptive trial designs.

Then the pandemic hit. For the first time in my career, I had to pivot entirely to remote work. At the end of the pandemic and with the return to work, I realize that my situation had changed, with my son being born in the pandemic and having found it easier to balance work-life with remote work, I realized the new way of working was what I was looking for. I also missed the HTA work and ITCs. So I explored companies dedicated to this model and in the space of consulting and HTAs, and that’s when Cytel caught my attention. Even better, they had an open position in the EVA department, where I could once again work on ITCs, strategic advising, and HTA submissions. That’s how I ended up here, and I’ve been enjoying myself ever since.

What do you like best about your role, and about working at Cytel?

I appreciate the diversity of projects we work on at Cytel and the numerous opportunities to collaborate across project teams. Recently, I was involved in a multi-team project where I had the chance to work alongside the Systematic Literature Review (SLR) and Comparative Effectiveness (CE) teams. This experience has been particularly exciting, as it allows me to see the full process — from evidence generation (SLR) to synthesis (my work) and its application in modeling (Health Economics, HE). Working on such projects is highly engaging as it provides the opportunity to collaborate with experts from different fields and discuss the best strategic approaches.

Additionally, Cytel’s flexible hours policy is ideal for both my work and personal life, allowing me to balance professional commitments with family responsibilities. With my wife working as a doctor in a remote area of Northern Manitoba and my little four-year-old at home, this flexibility is invaluable.

Your work involves advanced Indirect Treatment Comparisons (ITC) analytical techniques — could you walk us through some of the key methodologies you specialize in and their impact on clinical research?

ITC is a broad term that encompasses various methods used to better understand a product’s comparative effectiveness against others. Including all available treatments in a clinical trial is often not feasible, which is where ITCs provide value. They offer a methodological framework for conducting comparisons between all available treatments, accompanied by a thorough review of the risk of bias in such comparisons. The goal is to provide a comprehensive overview of how different treatments are performing, and direct comparisons between them, benefiting health review bodies, physicians, and researchers alike.

Having this summary also helps to identify populations with unmet needs, uncovering areas where specific subgroups may have lower treatment performance or where evidence (such as clinical trials) may be lacking for certain therapeutic options.

On a personal note, I’ve seen my wife, a doctor, discuss ITC results in osteoporosis at her university hospital, underscoring how critical this type of work is in guiding physicians to better understand their therapeutic options when supporting patients. Unfortunately, it wasn’t my research, though!

Given how fast analytics and data science evolve, how do you stay up to date with new methodologies and industry best practices? Are there particular resources, strategies, or mentors you rely on?

First, it’s important to recognize that the pharmaceutical industry, particularly health outcomes research, is an area where data science and the methodologies we apply are evolving at a rapid pace. The way we conduct comparative effectiveness and ITC now is vastly different from when I started my career. I’m a firm believer in self-learning to stay at the forefront of these changes. Thanks to platforms like LinkedIn, PubMed, and various conferences, there are always opportunities to discover new methodologies and engage in discussions to learn from others in the industry. By actively participating in these communities and keeping an open mind, I’ve managed to keep myself up to date with the latest developments — or at least, I hope so.

You’ve been deepening your expertise in machine learning (Python & R) and RShiny. How do you see these tools shaping the future of Comparative Effectiveness research?

Clinical research is advancing through tools like RShiny and machine learning, revolutionizing the field. Historically, there was often a significant delay between evidence generation (data collection) and evidence synthesis (such as ITCs and trial summaries). However, these new tools are helping to bridge that gap, bringing us closer to real-time evidence synthesis. For example, RShiny allows us to explore different model specifications in real-time and assess their impact on ITCs, providing a framework to build dynamic dashboards. Machine learning is also enhancing our capabilities in SLRs, significantly reducing the time required to locate relevant publications and extract key information.

Despite these advancements, challenges remain. For instance, generative AI tends to produce “hallucinations,” making the output occasionally unreliable, and RShiny demands substantial upfront work. However, I am confident that these issues can be overcome, and such tools will ultimately shape the future of comparative effectiveness.

What makes Comparative Effectiveness so important, and how does it influence clinical research outcomes?

In my view, comparative effectiveness is a critical component of health research, alongside clinical trials and health economics modeling. Without this component, understanding the full range of treatment options becomes challenging, often leading to qualitative choices rather than evidence-based decisions. As research and increasing life expectancy have demonstrated, evidence-based decision-making is essential — not only to make the right choice but to do so consistently. In this context, comparative effectiveness provides valuable insights by helping us understand how treatments rank, identify the most appropriate treatment strategies based on patient characteristics, and model the costs and opportunities of new treatments. This enables the development of sustainable healthcare strategies that are grounded in replicable science, rather than individual decisions, and ensures that clinical research can effectively translate into real-world applications.

Could you share a project you have worked on that you feel particularly proud of, and why?

This is particularly challenging for me to answer, as I take great pride in many of the projects I have worked on. However, one project that stands out is our recent work on Multilevel Network Meta-Regression (ML NMR) in an indication that is still in its early stages. The unique aspect of using the ML NMR approach was its ability to provide deeper insights into how different treatments’ efficacy are affected based on their baseline characteristics. Given that this disease area is still emerging, there is a significant unmet need among patients, and it was crucial for us to understand how their baseline traits might influence the effectiveness of various treatment options, especially as different treatment classes are available for it.

Through this work, we were able to identify specific subgroups of patients who would benefit most from certain treatments, while suggesting alternative treatment classes for others. Our client was exceptional in their commitment — not only did they want to understand the comparative effectiveness of their product, but they also collaborated closely with the academic community to ensure that the guidelines evolved to reflect their new findings. Despite the complex methodology involved, the insights we gained were invaluable. Collaborating with the client to enhance guidelines and support their product is a source of immense pride for me. It also highlights the high caliber of clients that Cytel is privileged to work with.

As a remote employee, how do you maintain a healthy work-life balance? What strategies work for you, and do you feel supported by Cytel in this regard?

First, I think it’s important to define what a healthy work-life balance means for me. I am passionate about my work, but I also live in the great north, where nature can be unpredictable. For me, a healthy work-life balance means maintaining a productive day while also being present for my family when needed. For example, I want to be available if my child needs to come home early due to a snowstorm. If my wife needs to travel to a nearby community due to an outbreak or to help manage the aftermath of a wildfire, I want the flexibility to travel to her over the weekend.

In this context, Cytel provides many options to help maintain a work-life balance, offering flexible work hours and a management team that listens. For instance, I can block time to pick up my child or adjust my schedule to finish early on Fridays if I need to travel. This flexibility has truly helped me balance my family and professional life. Additionally, I make sure to have a dedicated workspace, which allows me to quickly get back into work mode and stay focused.

What are your main interests outside of work?

I have been a traveler for many years, having left my parents’ house to study very early and never settled in one place for more than five years ever since. As a result, I have always been passionate about discovering the cultures of the places I’ve lived in. You can often find me spending a lot of time in museums — especially now that my child can handle longer visits.

Beyond that, I am also deeply interested in supporting the communities I’ve been a part of. This has led me to actively participate in local clean-up initiatives, social events for the elderly, and various activities that allow me to give back to the communities that have welcomed me.

Finally, what’s one piece of career advice you wish you had received earlier?

The piece of advice I was given was, “You work to live, not live to work.” Most people misinterpret this as saying we shouldn’t be excited about our work. However, as my manager explained to me, it’s perfectly fine to be passionate about work but we should also take time to live, whether that means resting, pursuing hobbies, or spending time with family. Work will always be there; new projects and opportunities will come. By cultivating balance, we can excel and build a long, sustainable career by learning to manage all aspects of life effectively.

Thank you, Victor, for sharing your experience!

Simulating Survival Outcomes for Unanchored Simulated Treatment Comparisons: Guidance on Censoring Approaches

Unanchored simulated treatment comparisons (STCs) are a valuable tool for manufacturers navigating the health technology assessment (HTA) landscape. When head-to-head clinical trials are unavailable, STCs allow for population-adjusted indirect comparisons between a single-arm trial and an external control arm.

Using regression modeling to predict outcomes based on patient characteristics, STCs enable comparisons in the absence of a common comparator. This is particularly valuable when evaluating novel therapies, especially in rare or specialized disease areas where randomized controlled trials may be limited.

Artificial Intelligence Applications in HEOR

Written by Reza Jafar, Omar Irfan, and Maria Rizzo

Recent advancements in machine learning (ML) and artificial intelligence (AI) can offer tremendous potential benefits to health economics and outcomes research (HEOR), such as in cohort selection, feature selection, predictive analytics, causal inference, and economic evaluation.[1] The use of ML and AI has been previously explored in systematic literature reviews (SLRs), real-world evidence (RWE), economic modeling, and medical writing.[2-4]

In this article, we assess the evolving landscape of evidence and developments attributed to AI in HEOR, reflecting on recent insights and developments presented at the 2024 US conference for The Professional Society for Health Economics and Outcomes Research (ISPOR) in Atlanta. Read more »

Unravelling PICO: The Pillars of the European Joint Clinical Assessment

The European Union (EU) health technology assessment (HTA) regulation aims to improve the availability of innovative technologies for patients across the European Union (EU).¹ It is also claimed to offer efficiency gains for manufacturers, due to one single EU-level submission vs. multiple parallel submissions to different HTA bodies.² In this blog, we will first introduce the Joint Clinical Assessment (JCA), a legal requirement of the EU HTA regulation; the pillars that hold the JCA together, the PICO framework; and the consequential impact to manufacturers on reporting requirements based on multiple PICO.

The Need for a “Living” Approach to HTAs

U.S. Drug Pricing Reform: Potential Impact on Pharma HEOR Evidence Generation

On August 16, 2022, President Biden signed into law the Inflation Reduction Act of 2022, which includes U.S. drug pricing reform that, among other things, requires the federal government to negotiate prices for some high-cost drugs covered under Medicare. In our recent webinar, Vice President of Value and Access Anna Forsythe discusses the effect that a strong HEOR strategy can have to help sponsors navigate this space for the benefit of all parties.

Real-Life Data-Sharing and EU Joint Clinical Assessments: Is Closing this Chasm a Mission Impossible?

Written by Grammati Sarri, David Smalbrugge, Andreas Freitag, and Evie Merinopoulou

The vision of a single, centralized system for the comparative joint clinical assessments (JCA) of health technologies in the European Union (EU) is now a reality, with corresponding guidance that supports EU member state cooperation and increases transparency in clinical assessments. However, oversight by the European Network for Health Technology Assessment (EUnetHTA) 21 will cease in September 2023, leaving stakeholders (pharmaceutical companies, local decision-makers) uncertain about next steps for the EU JCA guidance, particularly regarding the use and weight of real-world evidence (RWE) and its opportunities in decision-making.
Read more »

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Why a dedicated reporting guideline is needed

What is ELEVATE-GenAI?

Reporting generative AI across HEOR workflows: The 10 ELEVATE domains

From framework to practice: The ELEVATE checklist

Why ELEVATE-GenAI matters for HEOR

Final takeaways

Interested in learning more?

Why this matters now

A bridge to HEOR: Learning from regulatory leadership

Four principles with outsized impact on HEOR

From drug development to HTA: An opportunity not to miss

Looking ahead

Interested in learning more?

What are CAR T-cell therapies?

CAR T-cell therapies: Highlighting equity concerns in access to innovative treatments

Exploring equity factors in HTAs of CAR T-cell therapies and the journey toward inclusive access

Addressing equity concerns in CAR T-cell therapies: Strategies for inclusive access

Can you give us a little background on your career so far? What inspired you to pursue a degree as a statistical engineer and how did that lead to a career as a research analyst in health economics?

What do you like best about your role, and about working at Cytel?

Your work involves advanced Indirect Treatment Comparisons (ITC) analytical techniques — could you walk us through some of the key methodologies you specialize in and their impact on clinical research?

Given how fast analytics and data science evolve, how do you stay up to date with new methodologies and industry best practices? Are there particular resources, strategies, or mentors you rely on?

You’ve been deepening your expertise in machine learning (Python & R) and RShiny. How do you see these tools shaping the future of Comparative Effectiveness research?

What makes Comparative Effectiveness so important, and how does it influence clinical research outcomes?

As a remote employee, how do you maintain a healthy work-life balance? What strategies work for you, and do you feel supported by Cytel in this regard?

What are your main interests outside of work?

Finally, what’s one piece of career advice you wish you had received earlier?