The call for clinical trials methodology demonstration projects aims to show the usability and capability of the innovative statistical methodologies for clinical trials in rare diseases, which have not been demonstrated on existing data for specific rare disease clinical trials yet. Necessary for a successful demonstration project is the collection of sufficient data from a rare disease clinical trial and related information to enable demonstration of the practicability, performance and opportunities of the innovative methodologies applied to these already acquired data. Trials are often performed with standard classical methodologies not specific for rare diseases resulting in a loss of power to show positive effects.
The aim of this demonstration call is not to reanalyse or question the original analysis of data from randomized controlled clinical trials, where efficacy was established, but rather to re-evaluate data that lacked efficiency because it was analysed with classical statistical methodology, which might be not feasible for trials in the rare disease context.
3 projects were selected and funded in 2020:
Disease area: Epidermolysis bullosa (ERN-SKIN)
Statistical methodologies: longitudinal data analysis, and multiple endpoints analysis
Statistical methodologists: Geert Molenberghs, KULeuven, Belgium; and Mats Karlsson, UU, Sweden
Epidermolysis bullosa simplex (EBS) is a rare genetic, blistering skin disease for which thereis no cure. Treatments that address the pathophysiology of EBS are needed.
In a randomized, placebo-controlled, 2-period crossover phase 2/3 trial, we assessed the immunomodulatory impact of 1% diacerein cream vs.placebo in reducing the number of blisters in EBS. 15 patients were randomized to either placebo or diacerein for a 4-week treatment and a 3-month follow-up in period 1. After a washout, patients were crossed over during period 2.Of the patients receiving diacerein, 86%in episode 1 and 37.5%in episode 2met the primary end point, i.e. a reduction of number of blisters by more than 40% from baseline in selected areas over the treatment episode(vs 14% and 17% with placebo, respectively). No adverse effects were observed.
An international consortium of statisticians with different, overlapping and complementing, areas of expertise, will (A) reanalyze the data using different state-of-the-art methodologies, trying to exploit the longitudinal nature of the data as much aspossible, (B) investigate the impact that certain characteristics of the trial have on the statistical analysis, (C) develop strategies and design recommendations for future trials in this area, but also transferableto other rare diseases, and (D), as means to ensure transferability and high dissemination of the results,devise computational tools that can be used by practitioners in order to implement (A) -(C) in concrete trial planningand analysis, and provide educational material.
Disease area: Progressive supranuclear palsy
Statistical methodologies: composite endpoint evaluation, develop prediction models, use historical longitudinal data
Statistical methodologists: Martin Posch and Franz König, MUV, Austria; and Mats Karlsson, UU, Sweden
Background: Progressive supranuclear palsy (PSP) is a neurodegenerative disease with a prevalence of 1-9/100,000 and an average disease duration of approximately 8 years. Effective symptomatic treatments and disease-modifying therapies are not available. Therefore, development of innovative therapies to slow down or halt disease progression are urgently required. Numerous investigational new drugsare entering the clinical trial pipeline for evaluation of their safety and efficacy. The experience and data generated by the consortium coordinator with the past clinical trials in PSP has demonstrated limitations of previously used methodologies, including heterogeneity in disease progression rates, limitations of the primary endpoint, limitations resulting from the short observation period and reluctance to be randomized to placebo. Identification of these limitations opened up ways forward to improve methodologies for future trials.
Aim: The applicants therefore aim to optimizeclinical trial methodology for PSP.
Methods: The consortium will develop more sensitive and more relevant endpoints (modified outcome measures, composite endpoints), develop prediction models,more efficient trial designs(adaptive designs, delayed-start designs, longer duration trials, platform trials, basket trials), and use historical longitudinal data for multiple outcome measures to improve statistical power.
Perspective: Applying the statistical methods developed by the applicants will help to improve the trial design and the statistical analysis methods for future trials in PSP.
Disease area: Tuberous sclerosis complex (ERN-EPICARE)
Statistical methodologies: bias assessment linked to randomization, extrapolation, and use of external data
Statistical methodologists: Ralf-Dieter Hilgers, UKA, Germany; and Holger Dette, RUB, Germany
Tuberous sclerosis complex (TSC), affecting 1 in 6.000 live births, is characterized by the development of multisystem tumors. Seizures are frequent up to 80% of individuals. They usually start in infancy and are often drug resistant, with a high risk of intellectual disability and autism spectrum disorders. In animal models, preventive treatment before seizures onset significantly decreased the risk of epilepsy as well as associated comorbidities.
EPISTOP randomized clinical trial (RCT) aimed to validate the effect of preventive therapy in patients with TSC diagnosed before clinical seizures with abnormal EEG, versus late standard therapy of epilepsy, administered after the seizures onset. This preventive therapy resulted in a significant better outcome inseizures and co-morbidities. However, this trial included few patients and did not allow to fully explore the secondary endpoints.
Our goal within EPISTOP-IDEAL project is to benefit from joining clinical expertise of EPISTOP project and experts from IDEAL EU project on methodologies for CTs in small populations in order to consolidate the results of EPISTOP CT using uncertainty evaluation of the existing data of randomized and observational arms and adding important information from external data collected after EPISTOP ended.
This collaboration aims to an optimal use of all available data (RCT, observational and external data collected with the same protocol). The goal is to demonstrate the added value of these methodologies in TSC CT and to their further use to rare epilepsies, and other rare diseases.