Net Treatment Benefit in rare disease trials: Aligning trial design with clinically meaningful outcomes

By definition, rare disease trials are conducted under obvious structural constraints. Patient populations are extremely limited, making recruitment challenging, and the clinical profile of patients is often heterogeneous. An analysis of 199 discontinued rare disease trials found that insufficient patient accrual was the leading cause of discontinuation, accounting for up to one third of cases.¹ In this context, trials should be designed to maximise the information obtained from each enrolled patient.

Despite this, many rare disease trials continue to rely on a single outcome to assess efficacy and determine success or failure. While multiple outcomes are collected, they typically play limited or no formal role in the treatment efficacy conclusion or regulatory approval. This conventional approach does not always reflect how treatment benefit is experienced in rare diseases, where effects are multidimensional and trade-offs between efficacy, safety, and quality of life are central to decision making.

Limitations of single endpoint designs in rare diseases

Traditional superiority trials are powered on one primary outcome. If that endpoint does not meet its predefined significance threshold, the trial is considered negative, even if improvements are observed in other clinically meaningful domains.

In rare diseases, this creates two structural challenges.

First, focusing on a single endpoint underutilises available data. Trials frequently collect information on functional status, patient-reported outcomes, adverse events, and event-based outcomes such as hospitalisations. When excluded from the primary analysis framework, their contribution to the overall assessment of benefit/risk profile is minimal.

Second, powering a study on one outcome may require larger sample sizes than are realistically achievable in small populations. When recruitment is difficult and patient numbers are fixed by epidemiology, improving statistical efficiency becomes essential.

These constraints have led to growing interest in methodologies that formally integrate multiple outcomes into a single assessment of treatment effect.

Outcome selection and prioritisation

A multidimensional analysis begins with structured outcome selection. Clinically meaningful endpoints must be identified based on disease biology, treatment mechanism, and stakeholder input. These may include survival, organ function, symptom burden, functional capacity, patient reported quality of life, or safety outcomes.

However, selection alone is insufficient. Outcomes must also be prioritised. Not all endpoints carry equal clinical importance. In progressive conditions, mortality or irreversible organ damage may take precedence. In other settings, maintaining independence or minimising severe adverse events may rank higher.

Prioritisation requires explicit decisions. Sponsors increasingly engage patient advocates, clinicians, and sometimes payers to define a hierarchy reflecting shared views of clinical value. Once established, this hierarchy can be embedded directly into the statistical analysis plan, either as a multidimensional primary endpoint or as a key secondary endpoint complementing a conventional primary analysis.

The ordering of outcomes directly determines how treatment benefit is evaluated, making stakeholder priorities operational within the design.

What is the Net Treatment Benefit?

Net Treatment Benefit (NTB), estimated using the Generalised Pairwise Comparisons (GPC) methodology, is designed to implement such a hierarchy.

The method forms all possible pairwise comparisons between patients in the treatment group and patients in the control group of a randomised clinical trial. For each pair, outcomes are assessed sequentially according to the predefined priority order. The comparison starts with the highest ranked endpoint. If a clinically meaningful difference is observed, the pair is classified as favourable or unfavourable for treatment. If not, the comparison proceeds to the next outcome in the hierarchy.

After evaluating all pairs, NTB is estimated as the difference between the probability that a patient randomly selected from the experimental group has a more favorable overall outcome than a randomly selected patient from the control group. The result is a single summary metric reflecting the overall benefit risk profile across all prioritised endpoints. The statistical framework and properties of GPC have been described extensively in the methodological literature.²

Statistical efficiency in small populations

By incorporating multiple clinically relevant outcomes into one unified analysis, NTB can improve statistical efficiency. Rather than relying on a single endpoint, the method leverages information across prioritised outcomes. Clinically meaningful thresholds can be specified to distinguish trivial from important differences, improving interpretability.

This is particularly relevant in rare diseases, where expanding sample size is often not feasible. A design that extracts more information per participant can reduce the number of patients required to achieve adequate power, or increase the probability of detecting an effect within a fixed population.

An example from the rare disease field illustrates this potential. In a post hoc analysis of the Phase 3 COMET trial in Pompe disease, investigators applied a prioritised multidimensional approach incorporating forced vital capacity and the 6-minute walk test. While the original analysis did not demonstrate statistical superiority on the primary endpoint alone, the prioritised analysis provided evidence favouring avalglucosidase alfa over alglucosidase alfa.³ This case shows how integrating clinically relevant outcomes can alter interpretation of treatment effect in small samples.

Implications for regulatory and HTA evaluation

Regulatory authorities increasingly require comprehensive benefit risk assessments. A multidimensional endpoint defined through explicit prioritisation provides a structured way to present the overall clinical profile of a therapy.

For regulators, NTB offers a framework that integrates mortality, functional outcomes, patient reported outcomes, and safety within a single estimate. For health technology assessment bodies and payers, the same framework can clarify how a therapy performs across outcomes that influence long term resource use and quality of life.

When outcome selection and prioritisation are defined early in development, the evidence base supports continuity from trial design through regulatory submission and reimbursement discussions. The same hierarchy used to evaluate efficacy can underpin value dossiers and payer negotiations, aligning clinical evidence with market access strategy.

Rare disease trials require approaches that reflect both scientific rigour and structural constraints. Single endpoint designs may not fully capture multidimensional treatment effects or optimise statistical efficiency in small populations.

Net Treatment Benefit provides a formal framework to integrate multiple prioritised outcomes into a single, interpretable measure of overall treatment effect. By requiring explicit selection and ordering of clinically meaningful endpoints, it enables incorporation of patient and clinician priorities into trial design. By leveraging information across outcomes, it can improve efficiency when sample size expansion is limited.

As rare disease research evolves, methodologies that align endpoint design with stakeholder priorities and make fuller use of available data are likely to play an increasingly important role in regulatory and health technology assessment contexts.

References

(1) Rees, C. A. P., Pica, N., Monuteaux, M. C., & Bourgeois, F. T. (2019). Noncompletion and nonpublication of trials studying rare diseases: A cross sectional analysis. PLoS Medicine, 16(11), e1002966.

(2) Buyse, M., Verbeeck, J., Saad, E. D., De Backer, M., Deltuvaite Thomas, V., & Molenberghs, G. (Eds.). (2025). Handbook of Generalized Pairwise Comparisons: Methods for Patient Centric Analysis. Chapman and Hall/CRC.

(3) Verbeeck, J., Dirani, M., Bauer, J. W., Hilgers, R. D., Molenberghs, G., & Nabbout, R. (2023). Composite endpoints, including patient reported outcomes, in rare diseases. Orphanet Journal of Rare Diseases, 18, 262.

About the author

Tom Mann is clinical solutions engagement lead at One2Treat. He brings over 15 years of experience in tech start-ups and scale-ups, where he played a pivotal role in driving customer engagement, marketing initiatives, and strategic partnerships. With a strong background in SaaS companies, Mann has a deep understanding of customer needs.