By Hoora Moradian, Victor Laliman-Khara, Peter Wigfield, Michael Dolph, and Michael Groff

Global health technology assessment (HTA) bodies are setting higher standards for rigorous evidence to support access decisions. In this evolving landscape, generating meaningful health economic (HE) models and indirect treatment comparison (ITC) analysis is critical — particularly in rare and chronic disease settings. However, traditional modeling techniques often fall short, prompting the need for more advanced and adaptable approaches.

Here, we discuss finding the right method for various market access scenarios, given your indication, patient population characteristics, and data gaps.

Addressing heterogeneity across multiple studies

Victor Laliman-Khara

Problem: A sponsor has an asset that is entering a crowded market (3rd or 4th entrant), with a significant number of comparators and a related shift in standard of care. While network meta-analyses (NMA) are broadly accepted, one of the challenges is the limited possibility to adjust for between-study heterogeneity. While Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC) exist, they only allow for pairwise comparison, limiting comparative effectiveness to two treatments.

Solution: Population-adjusted indirect comparisons (PAICs) at the aggregate level — such as Multilevel Network Meta-Regression (ML-NMR) and Network Meta-Interpolation (NMI) — are increasingly used to address heterogeneity across multiple studies. These methods are particularly well-suited for situations where population adjustments are necessary across a network of trials, and where standard NMA falls short in accounting for treatment effect modifiers and between-study differences. Unlike traditional approaches such as MAIC or STC, which are limited to pairwise comparisons, ML-NMR and NMI extend the capability to more complex networks, enabling more robust and generalizable estimates.

Accurately reflecting treatment sequencing

Michael Groff

Problem: Many clients struggle to accurately reflect treatment sequencing in their decision problem modeling — especially in indications such as chronic inflammation with multiple therapeutic options. Capturing sequences can make it difficult to fully exploit the value proposition of a product, and poor structuring often leads to models that oversimplify the clinical pathway, become overly complex, or drive runaway costs that make the intervention appear less favorable.

Solution: Apply a structured, evidence-based methodology to transparently map and model clinical sequences, ensuring all clinically relevant transitions, such as disease progression, treatment pathways, and health state changes, are incorporated without compromising credibility, usability, or cost realism.

Handling pairwise comparisons in rare disease indications

Hoora Moradian

Problem: Assets in an indication where the referent trial and comparator trial have sparse data and/or heterogeneous populations present a challenge, especially in pairwise comparisons. This is common in rare disease indications, where standard PAIC approaches often struggle to produce reliable insights.

Solution: Apply innovative strategies for pairwise comparisons when traditional methods fall short:

• G-Computation, a flexible and well-established method, can be applied when there is poor covariate overlap between study populations.
• MAIC with random forest-based weighting can be used when the overlap is poor and the sample size is very small, delivering improved robustness over traditional methods.

Accounting for health equity

Mike Dolph

Problem: Accounting for health inequity is growing in importance in certain areas with a public health focus such as vaccination programs.

Solution: As health systems increasingly prioritize equity-informed decision-making, distributional cost-effectiveness analysis (DCEA) has emerged as an important extension of the traditional cost-effectiveness analysis, reflecting a growing recognition that efficiency alone is not enough to guide healthcare decisions. This approach allows decision-makers to quantify and weigh the trade-offs between improving overall health and reducing health inequalities.

Managing immature survival data

Peter Wigfield

Problem: Survival data, particularly in early-stage diseases, are often immature, and the emergence of more effective treatments is likely to prolong this trend.

Solution: State transition models (especially multistate frameworks) offer benefits that are difficult to achieve with conventional partitioned survival methods alone. With real-world evidence evolving rapidly, the relative ease of incorporating external evidence sources is a significant advantage, enabling the real-world value of a product to be realized. However, multiple limitations (particularly related to indirect treatment comparisons and model fits) also need to be considered.

Final takeaways

At Cytel, we specialize in solving complex evidence challenges, whether it’s navigating complex markets, rare diseases, or evolving treatment pathways. Our advanced methodologies, including ML-NMR, NMI, structured sequencing frameworks, equity-informed modeling (DCEA), and multistate survival models, empower sponsors to generate credible, realistic, and actionable insights. These tools go beyond traditional approaches to support better decisions and stronger value demonstration.

Interested in learning more?

Watch the authors’ recent webinar, “Leveraging the Right Advanced Quantitative Methods to Address Evidentiary Gaps,” on demand:

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