Time: 8:30 AM – 10:00 AM
Title: Statistical Challenges and Opportunities in the AI Agentic Era
Abstract: TBD
Title: Solving the Diagnostic Odyssey: Bridging Macro-Level EHR Subphenotypes and Micro-Level Missense Variants
The journey toward true precision medicine has long been fragmented by a vast analytical divide: we observe macro-level clinical presentation in our health systems, yet we isolate micro-level genetic variations in our laboratories, frequently failing to translate the two into a timely diagnosis. This disconnected paradigm directly fuels the global "diagnostic odyssey" for hundreds of millions of rare disease patients, where sparse documentation, clinical pleiotropy, and millions of genetic variants of uncertain significance leave up to half of all suspected monogenic conditions entirely unresolved. To bridge this chasm, we introduce a unified, open-source translational framework that connects longitudinal health system phenomics with molecular deep learning by simultaneously aligning the phenotypic and genomic scales. At the clinical macro-level, our approach utilizes a transformer architecture and an iterative, self-refining weak-supervision loop to decode messy, real-world electronic health records—moving beyond traditional binary tracking to map continuous disease evolution and uncover highly distinct, prognostically critical patient subphenotypes. At the molecular micro-level, the framework maps these rich clinical phenotypes directly to specific single amino acid alterations by leveraging multi-modal contrastive learning to align protein sequence features and medical knowledge graphs within a shared metric space. Validated on real-world diagnostic dilemmas within major hospital networks and the Undiagnosed Diseases Network, this integrated pipeline routinely surfaces the definitive clinical diagnosis out of thousands of possibilities and pinpoints the true causal missense mutation as a top candidate. By closing the loop from raw clinical trajectories to residue-level biophysical alterations, this paradigm shifts AI-driven medicine past abstract pathogenicity scores and into the realm of precise, actionable, and scalable diagnostic discovery.
Title: Discussion on Dynamic Bayesian Design and FDA Bayesian Methods Guidance
Abstract: TBD
Time: 10:30 AM – 12:10 PM
Session Title: Innovative Analysis Methods in Rare Disease Research
Title: A Bayesian Dynamic Borrowing Approach to Support Regulatory Approval of Marstacimab in Pediatric Hemophilia
Abstract: TBD
Title: Comparison of Methods for Handling Death and Missing Data in Survivors in the Analysis of Functional Outcomes in Amyotrophic Lateral Sclerosis (ALS)
There are different analysis strategies to handle death events in the analysis of primary functional outcomes in ALS clinical trials. We compare the performance of several methods such as joint ranking, joint modeling, and ordinal modeling, in terms of bias, type I error rate, and power with a focus on the impact of missing data. We illustrate that the handling of missing data in the original formulation of the joint rank test of function and survival, also called the Combined Analysis of Function and Survival (CAFS) in ALS, is not adequate, only controlling the type I error under the strong null hypothesis. We identify the overall best performing method based on simulated data consistent with ALS natural history data.
Title: Bayesian Mixed Models for Repeated Measures with Informative Priors
The mixed model for repeated measures (MMRM) is a standard
approach for analyzing continuous longitudinal endpoints in
clinical trials. Despite growing interest in Bayesian borrowing
in clinical trials, particularly in rare disease settings,
direct application to the MMRM has remained limited, relying,
when pursued, on custom implementations. Specifying informative
priors on clinically meaningful quantities in a model with
treatment-by-visit interactions and additional covariates as in
the MMRM is inherently difficult, and in practice borrowing is
often reduced to synthesizing evidence at a single time point in
a simpler Bayesian model, leaving the longitudinal structure
largely unexploited. This talk presents a workflow that
addresses these challenges, covering planning, fitting,
assessing, and reporting of Bayesian MMRMs with informative
priors. Central to the workflow is the concept of
informative prior archetypes: standardized model
reparameterizations in which fixed-effect parameters correspond
directly to clinically interpretable quantities, enabling
deliberate and transparent prior assignment across visits and
treatment arms. This correspondence reduces the gap between the
statistical model and domain knowledge, and facilitates prior
elicitation and communication of assumptions to clinical and
regulatory stakeholders. The R package brms.mmrm
provides a flexible implementation of the proposed workflow,
lowering the barrier to applying Bayesian MMRMs with informative
priors.
Title: Bayesian Hierarchical Dose-Response Model Averaging in Small Clinical Trials for Decision-Making
In cell and gene therapy (CGT) and rare diseases, early-phase (Phase 1/2) clinical trials face unique statistical challenges when making Go/No-Go decisions for pivotal (Phase 3) development. These trials typically involve a limited number of investigational dose levels and very small sample sizes at each dose level. Furthermore, the Phase 2 proof of concept part does not include a control group for comparison. As a result, inferences relying solely on observed data from each dose level are not only inefficient but also can introduce significant bias, leading to an increased risk of incorrect decisions on progressing to Phase 3 development. Bayesian techniques offer more effective and informative statistical methodologies by integrating prior knowledge from real-world data or natural history study data and enhancing flexibility in decision-making. We propose a Bayesian hierarchical dose-response model averaging (BHDRMA) method for small clinical trials with a continuous primary efficacy endpoint. The proposed approach integrates data across all dose levels from both parts of the Phase 1/2 study, incorporates historical control information into the prior, and employs posterior weighted dose-response estimates through Bayesian model averaging and posterior probability-based Bayesian decision criteria to aid in Go/No-Go decision-making. In a simulation study, the proposed BHDRMA method consistently improves the precision in dose-response estimation compared with the conventional parametric bootstrap model averaging approach, achieving substantial reductions in both average absolute prediction error and root-mean-square error.
Time: 1:10 PM – 3:00 PM
Session Title: AI and Machine Learning in Rare Disease Research
Title: Enhancing Statistical Power in Rare Disease Trials: Evaluating ML Causal Inference Methods Under Small Samples and Missing Data
Abstract: TBD
Title: Leveraging AI to Enhance Study Design for a Pivotal Phase 3 Rare Disease Program
Abstract: TBD
Title: From Modeling and Simulation to Decision: Architecting Human-AI Collaboration for Trial Design
Abstract: TBD
Title: Agents Statisticians Can Trust in Regulated Domains: Rare Disease Trial Design as a Stress Test
Abstract: TBD
Time: 3:30 PM – 5:10 PM
Session Title: Emerging Topics and Case Studies in Rare Disease Development
Title: TBD
Abstract: TBD
Title: TBD
Abstract: TBD
Title: Designing a Pivotal Trial Where a Placebo Is Impossible: External Controls and a Surrogate-Endpoint Accelerated-Approval Strategy for Gene Therapy in Sanfilippo Syndrome Type A
MPS IIIA (Sanfilippo syndrome type A) is an ultra-rare, fatal lysosomal storage disease of early childhood. Deficient sulfamidase lets heparan sulfate accumulate in the brain, driving relentless neuronal loss: affected children develop briefly, then regress, losing cognition, speech, and motor function, with death in the second decade. No therapy is approved.
That reality drives the design. A placebo arm is neither ethical nor feasible when decline is certain, and families will not accept sham gene therapy. The trial is therefore an open-label, single-arm study of a one-time intravenous AAV9 vector delivering a functional SGSH gene, with an external control drawn from two prospective natural history cohorts. Because treated infants were younger than the natural history patients, cognitive trajectories were compared using propensity-score weighting on baseline age and a non-linear growth-curve model, aligning the comparison on developmental slope rather than raw means.
The regulatory strategy runs on two tracks. Reduction of CSF heparan sulfate, the primary disease-causing biomarker, is treated as a surrogate reasonably likely to predict clinical benefit and serves as the basis for accelerated approval. The biomarker endpoint is primary in one region, with the neurodevelopmental endpoint primary in the other and the two transposed per health-authority feedback. Endpoints, analysis sets, the biomarker exposure metric, and the gated testing hierarchy were pre-specified in a statistical analysis plan aligned with regulatory agreements and finalized before database lock, with confirmatory testing on pooled data across the treatment and long-term follow-up phases.
Title: TBD
Abstract: TBD
Time: 8:30 AM – 10:00 AM
Title: Interim Monitoring in snSMART Designs with Multiple Endpoints
The small-n, Sequential, Multiple Assignment, Randomized Trial (snSMART) design improves efficiency in rare disease trials by allowing participant re-randomization across treatment stages. This talk extends the snSMART framework to address interim monitoring when multiple correlated endpoints jointly define trial success. We propose a Bayesian predictive probability of study success framework that supports early stopping for futility or efficacy and removal of ineffective treatment arms, while accounting for endpoint correlation through joint modeling. Simulation studies demonstrate that joint modeling outperforms independent endpoint analyses in Type I error control and adaptive arm selection.
Title: TBD
Abstract: TBD
Time: 10:30 AM – 12:10 PM
Session Title: Innovative Clinical Trial Design in Rare Diseases
Title: Reframing the BASIS Trial as a Basket Trial Master Protocol in Hemophilia A and B: Methodological Insights and Connections to Rare Disease Drug Development
Abstract: TBD
Title: A Quantitative Framework for the Design and Optimization of N-of-1 and N-of-Few Trials in Rare Neurological Diseases
The design of clinical trials for rare neurological diseases is challenging due to small and heterogeneous patient populations, limited natural history data, and the unique demands of disease-modifying therapies. This presentation introduces a quantitative framework for the systematic design and optimization of N-of-1 and N-of-few trials, study designs that are increasingly relevant for ultra-rare diseases and precision disease-modifying therapies such as antisense oligonucleotides and gene therapies, where evaluating treatment effects at the individual level is both scientifically compelling and often the only feasible approach. We will illustrate the framework's utility by in-silico comparison of several design and analysis strategies for a hypothetical disease-modifying therapy trial in Autosomal-Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS).
Title: From Dose Finding to Decision Making: Comparing 3+3, BOIN, and BF-BOIN Designs and Integrating Go/No-Go Criteria in Early-Phase Development
Choosing the appropriate study design is critical in early-phase drug development as it affects how efficiently and reliably a safe and effective dose can be identified. The conventional 3+3 design is simple to implement but it is often not statistically efficient; it may select an inappropriate dose and does not fully leverage accumulating data to guide decision-making. A more robust design can identify the optimal dose more efficiently and with fewer patients, shortening development timelines and supporting regulatory submission. Beyond the study design, a clear decision rule is needed to determine whether a program should continue. An innovative Bayesian Go/No-Go framework can be used to establish pre-specified rules for declaring Go, No-Go, or Indeterminate. These rules should be defined before data review and based on clinically and commercially meaningful thresholds, enabling faster and more transparent decisions.
We present a simulation-based comparison of 3+3, the Bayesian Optimal Interval (BOIN) design, and its backfilling extension (BF-BOIN) across different scenarios. In our simulations, BOIN and BF-BOIN picked the correct dose much more often than 3+3 and stopped early less often. Sample sizes were similar across all three designs, so the better accuracy did not require a bigger trial. BF-BOIN also collects more safety data through backfilling, though it is more complex to perform. We also apply the Bayesian Go/No-Go framework to the expansion phase using different thresholds and prior distributions to assess assurance and determine whether the program should stop or continue while maintaining transparency and statistical rigor.
In conclusion, model-assisted designs and Bayesian decision rules provide a robust and operationally efficient framework for accelerating early-phase development. This innovative approach can help sponsors avoid continued investment in drugs unlikely to succeed, provide regulators with a clearer rationale, and support patients to access effective treatments sooner.
Title: TBD
Abstract: TBD