AIM OF THE CALL (call documents here)
The innovation methodologies topics particularly include (but are not limited to):
- Development of a disease progression model from a natural history cohort or other observational studies.
- Development and validation of a disease specific clinically meaningful outcome with special interest in PCOMs, or composite endpoints.
- Development of a design and analysis procedure for a pharmacometric model and/or bridging study.
- Development of a randomization-based model as an alternative analysis strategy and explore the level of evidence.
The call intends to privilege support of collaborations among groups of experts consisting of different stakeholders including methodological experts, clinicians, patients and industry (when relevant) who will jointly develop innovative methods to fill gaps of RD clinical trial methodologies. The aim is, to have ready to use methods, which will drive regulatory decision-making.
Title: Innovative Statistical Methodologies to Improve Rare Diseases Clinical Trials in Limited Populations
Principal Investigator: Nabbout Rima, MD, PhD, Prof.
Biostatistican: Prof. Dr. Holger Dette – Prof. Dr. Geert Molenberghs – Prof. Dr. Ralf-Dieter Hilgers – Dr.Georg Zimmermann
Involved external Partners: Frank Bretz (Novartis), Klaus Romeo (C-Path)
Pharmaceutical Product involved: no
Summary: Epileptic and developmental encephalopathies are a group of rare and ultra-rare epilepsies, often due to genetic etiologies. Patients present drug-resistant seizures with neurodevelopmental, psychiatric, sleep, eating and motor disorders of different severity. This heterogeneity further divides these syndromes in ultra-rare subgroups and makes it difficult to conduct therapeutic trials with a high level of evidence. Furthermore, the evaluation of efficacy in the few performed trials was based on the reduction of seizures frequency, which seems very reductive regarding the complex clinical picture of the patients. Moreover, due to the patients’ condition, missing data are common in these longitudinal studies. To fill these gaps, we will develop new methodologies for the comparison of response profiles from the entire population of rare epilepsies with the corresponding profile of a rare and ultra-subgroup and methodology applicable to count data (based on maximum deviation approach and bootstrap tests). We will also implement joint analyses of several possibly incomplete longitudinally observed outcomes, in the context of small sample sizes (based on standard longitudinal methodology, surrogate endpoint evaluation methodology, local influence methodology and ANCOVA-like methods will be studied, with the aim of increasing power by covariate adjustment). Finally, we will evaluate the level of evidence based on population and randomization-based inference, linked to randomization procedure in setting of trials with composite endpoint in limited populations.
Title: Creating robust evidence for longitudinal progression changes and treatment effects in ultrarare neurological diseases: the case of multisystemic autosomal-recessive ataxias
Principal Investigator: Synofzik, Matthis (Co-PI: Rebecca Schüle)
Biostatistican: Prof. Dr. Mats KARLSSON (Co-PI: Sebastian Ueckert) – Prof. Dr. France MENTRE – Prof. Dr. Ralf-Dieter Hilgers (Co-PI: Nicole Heussen)
Involved Industry: Alex SVERDLOV (Novartis), Yevgen RYEZNIK (Astra Zeneca)
Pharmaceutical Product involved: no
Summary: There is a scarcity of robust trial design and analysis methods in ultra-rare neurological diseases (RND) affecting small cohorts (< 50)or even single cases. The urgency to find solutions is accelerated as disease-modifying compounds are now on the horizon for a growing number of RNDs, yet methods to generate robust evidence for treatment effects are missing. EVIDENCE-RND will develop a toolbox of innovative statistical methodologies designed to fill this gap. Our methods toolbox will deliver: (i) an optimized clinically meaningful composite endpoint for RNDs, including methods to evaluate applicability across ultra-rare RND subpopulations; (ii) robust progression models, including prediction of individual disease trajectories and quantification of their uncertainty; (iii) pharmacometrics models to analyse treatment efficacy and guide trial design and treatment decisions even in n-of-1 studies; (iv) randomization-based inference models for valid and efficient RCTs in ultra-small cohorts. As showcase, we will apply them to autosomal-recessive cerebellar ataxias, leveraging registry datasets from major transatlantic consortia (PREPARE, PROSPAX, EUROSCA, ESMI), close communication with patient organisations (Euro-Ataxia) and industry trial expertise (Novartis, AstraZeneca). Continuous interaction with EMA and C-Path will ensure that the overall goal will be readily achieved: to provide EMA with proposals for optimized outcomes, trial designs, and innovative analyses, optimized to be as informative as possible to allow robust evidence in the smallest possible populations.