EPISTOP-IDEAL (EPISTOP clinical trial data set re-use with statistical methodologies tailored
for clinical trials in rare diseases).aims to show the usability and capability of the newly
developed statistical methodologies for clinical trials in rare diseases and how they can
help to better consolidate the results of the randomized clinical trials (RCT). CTs are often
performed with standard classical methodologies not specific for rare diseases resulting in a
loss of power to show positive effects or in the incapacity to answer all the questions that
patients, clinicians and regulatory agencies need to obtain from therapy trials. This call
will benefit from an exceptional set of data collected longitudinally in patients with TSC
during EPISTOP project (2013-2019) and beyond as the clinical follow-up of patients is
continuing following the EPISTOP protocol and using the same drug (vigabatrin) with the same
doses range. EPISTOP-IDEAL aims, through a collaboration between EPISTOP coordinator and
members and expert statisticians from the IDEAL (Integrated DEsign and AnaLysis of clinical
trials in Small population groups, a previous EU project on methodologies developed for
trials in small populations), to consolidate the primary end point results of the trial using
bias assessment and external data and to strengthen the results of some major secondary end
points that were not conclusive because of the small number of the sample. This project is
based on collaboration between experts in TSC, a rare disease with high morbidity, and
clinical trials methodologies' experts that developed innovative methodologies that can
re-evaluate data that might have lacked partly efficiency because it was analysed with
classical statistical methodology as RCTs. EPISTOP-IDEAL will aim to re-use the data
collected during EPISTOP trial that compared the impact of early (preventive) or late
(standard) treatment by Vigabatrin (the drug usually used to treat early onset epilepsies in
patients with TSC) on the primary end point of the trial (time to seizure onset) between 2
groups of infants enrolled in this trial before the onset of seizures. In addition to these
data, similar data have been acquired since the EPISTOP end of recruitment in 2018. These
external data are available in the health institutions involved in this call and will be
collected and used to provide additional external data. The EPISTOP-IDEAL project will
include 5 partners showing complementarity in expertise and including the coordinator of
EPISTOP, the WP leader of the EPISTOP clinical trial, one member of EPISTOP consortium and 2
experts in methodologies that developed the innovative methodologies that will be used in
EPISTOP-IDEAL.
Description of the unmet need(s) addressed One hundred and one infants were enrolled in the
EPISTOP project. Seven children were later excluded due to misdiagnosis. The intention was to
randomize all infants with EEG abnormalities into one of two arms: one received ASM with
vigabatrin immediately after epileptic changes on EEG, and the second received the same drug
with the same range of dose later, after the onset of seizures. However, in some countries
the randomized study in infants was not approved by the ethics committee and the study had to
be conducted with two subgroups: observational and randomized subgroups. In both, the same
criteria for the diagnosis of epileptic changes on EEG, the same criteria for preventive or
standard treatment, and the same ASM (vigabatrin) were used. However, the number of the
children participating in the randomized arm of EPISTOP decreased to 53. Moreover, in some
children preventive treatment was not feasible due to seizures' early onset before the EEG
recording and the randomization and some others dropped-out during 24-month- long follow-up.
Therefore, 26 children completed the study in the randomized arm and 24 in the observational
subgroup. The primary endpoint of the project was the time to first seizure and even though
the groups were smaller than expected, this primary endpoint was met. With respect to the
statistical evaluation of the data, the EPISTOP consortium observed various effects in the
data, which could not be confirmed yet, because of a gap in standard statistical analysis
methodologies. IDEAL's findings are considered to be helpful in this context so that a unique
opportunity was to apply to a joint project with the IDEAL's experts. The formed consortium
will explore the effect of innovative methodologies in these settings and serves as a nucleus
for future studies in Epilepsy as well as similar rare disease areas. Aspects of reanalysis
include a) Assess the level of evidence linked to randomization procedures, b) Rigorous use
of methods for extrapolation, c) Identify most sensitive response variables and d) Assess and
overcome uncertainty in estimate. After EPISTOP enrolment completion, most health
institutions involved in EPISTOP continued the use of EPISTOP protocol for the clinical best
care of infants with TSC. Therefore, there are patients with TSC followed with the same
protocol, using depending on the centre the preventive or the standard treatment and that we
can propose as external data for validation of EPISTOP results Description of the proposed
innovative methodological analysis. To use the innovative statistical methods to design and analyse small population clinical
trials developed under the umbrella of the IDEAL project (Hilgers et al., 2018) the following
methods are considered:
a) Development regarding level of evidence linked to randomization procedures b) Rigorous use
of methods for extrapolation c) Methods to identify trial most sensitive response variables
d) Methods to assess and overcome uncertainty in estimates. 1. The data from the randomized clinical trial of the EPISTOP project will be used to
investigate the impact of the allocation process on the level of evidence. In particular
we will specify a bias model using the biasing policy and relate the model to the
allocation sequence resulting from the applied randomization procedure. Based on this we
are able to quantify the impact of bias on the study results expressed as p-values as
well as confidence intervals (Hilgers et al., 2018). This can be interpreted as
uncertainty evaluation of trial results. We will then use the model to derive the biased
corrected test result on the primary endpoint (Rückbeil,Hilgers and Heussen, 2019).
This is the part of the reanalysis of the randomized trial data. The findings of the
reanalysis can be used in two further directions. As we know an estimate of the
potential impact of bias on the study result, we will derive recommendations for
planning future trials. This concerns the formal evaluation of randomization procedures
to identify the recommended procedure protecting against bias in a future study. On the
other hand, we use the same approach to quantify the impact of bias in the observational
part of the EPISTOP project. If the resulting amount of bias identified in the
randomized and the observational trial correspond to each other, we are allowed to pool
both trials to increase the level of evidence. We will use individual patient data meta
analytic methods, e.g. using the amount of Bias as weighting factor and compare this to
other approaches described below. The approach is beyond the recommendation of the
Cochrane Collaboration, where the evaluation of bias is on a qualitative level. Bias may
cause potential unobserved heterogeneity which could be detected with the method at
hand.
2. Further, within the IDEAL project some innovative statistical methodologies were
developed to compare two or more groups [references] which are useful to reanalyze the
data of the EPISTOP in different directions and with a possible increase in statistical
evidence from the data. These groups can either correspond to two treatments in one
clinical trial with the goal to detect differences, or they can correspond to different
clinical studies with the goal to combine the data from different resources. In the
project we will demonstrate the new methodology in both situations. We will compare the
effect of medication in the two treatment groups (early versus late treatment) in a
better way using statistical tests for comparing curves and surfaces. Starting with the
primary endpoint (time to first seizure), we will compare the curves estimated from the
Cox proportional hazard model (percentage of seizure-free patients and hazard function)
corresponding to different groups. For this purpose, we will extend the maximal
deviation approach developed in Bretz et al. (Bretz et al., 2018), Dette et al. (Dette
et al., 2018), Collignon et al. (Collignon, Moellenhoff and Dette, 2019) and Möllenhoff
et al. (Moellenhoff et al.,2018; Möllenhoff, Bretz and Dette, 2019), such that it can be
applied to EPISTOP data. In a second step, secondary endpoints (such as time to clinical
seizures, time with seizures, risk of hypsarrhythmia and infantile spasms, time to
second drug and to seizure onset from first abnormal EEG) will be investigated. A
particular focus in these considerations is the inclusion of covariates (such as tuber
volume, RML volume, age etc.), which results in the comparison of surfaces. Similar
clinical and EEG data were gathered in different centers of the EPISTOP consortium for
patients not included in the trial. The methodology developed in IDEAL will be used to
validate, if these data can be included in the EPISTOP study to conduct statistical
inference based on larger data sets.
3. To reanalyze response data to treatment, for the randomized as well as for the
observational trial separate longitudinal models will be applied, including Cox
regression models to model e.g. recurrent clinical seizure events to understand the
disease progression. With respect to score like the EEG score longitudinal data
modelling is biased by ceiled or floored effects of data. This can be accounted for in
nonlinear mixed effects models which are currently applied to SARA score in Friedreich
Ataxia patients (Reetz et al., 2016) (Hilgers, work in progress). The improved model
building will be used to illustrate projections about sample size calculation similar to
the work successfully applied in Friedrich Ataxia (Reetz et al., 2019).
Finally, we will use model averaging methods, bootstrap and permutation test methodologies in
advanced statistical models to overcome the uncertainty caused by bias in measurements. As
this methodology is underdeveloped right now, it combines the research of the two IDEAL
expert groups leaded by Professor Dette and Professor Hilgers.
