Designing an exploratory phase 2b platform trial in NASH with correlated co-primary binary endpoints Elias Laurin Meyer1 Peter Mesenbrink2 Nicholas A. Di Prospero3 Juan M. Peric as45

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Designing an exploratory phase 2b platform trial in NASH with

correlated, co-primary binary endpoints

Elias Laurin Meyer1, Peter Mesenbrink2, Nicholas A. Di Prospero3, Juan M. Peric`as4,5,

Ekkehard Glimm6,7, Vlad Ratziu8, Elena Sena4, and Franz K¨onig1,*

on behalf of the EU-PEARL NASH Investigators+

1Center for Medical Data Science, Medical University of Vienna, Austria

2Novartis Pharmaceuticals Corporation, One Health Plaza, East Hanover, NJ, USA

3Janssen Research and Development, Raritan, NJ, USA

4Liver Unit, Internal Medicine Department, Vall d’Hebron University Hospital, Vall d’Hebron Institute for Research

(VHIR), 08036 Barcelona, Spain

Centros de Investigaci´on Biom´edica en Red Enfermedades Hep´aticas y Digestivas (CIBERehd), ISCIII, Madrid, Spain

6Novartis Pharma AG, Basel, Switzerland

7Institute of Biometry and Medical Informatics, University of Magdeburg, Germany

8Assistance Publique-Hˆopitaux de Paris, Hˆopital Pitie-Salpetriere, University of Paris, Paris, France

+The list of investigators is shown in the Acknowledgements

*Correspondence: franz.koenig@meduniwien.ac.at; Tel.: +43-1-40400-74800

Abstract

Non-alcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease (NAFLD)

and a disease with high unmet medical need. Platform trials provide great beneﬁts for sponsors and trial

participants in terms of accelerating drug development programs. In this article, we describe some of the

activities of the EU-PEARL consortium (EU Patient-cEntric clinicAl tRial pLatforms) regarding the use

of platform trials in NASH, in particular the proposed trial design, decision rules and simulation results.

For a set of assumptions, we present the results of a simulation study recently discussed with two health

authorities and the learnings from these meetings from a trial design perspective. Since the proposed design

uses co-primary binary endpoints, we furthermore discuss the diﬀerent options and practical considerations

for simulating correlated binary endpoints.

1 Introduction

The recent years have seen unprecedented challenges for many branches of modern medical research. The

desire to accelerate development and approval of new treatments has called into question some long-standing

drug development paradigms, such as the strict succession of phase 1, 2 and 3 trials and the insistence on

separate trials for every experimental compound [

]. Consequently, substantial eﬀort has been made into

the development of master protocol trials and in particular platform trials [

–

]. These types of trials allow

evaluation of many investigational treatments in parallel and hence their implementation has increased over

the last years. The interest in platform trials has increased further with the emergence of the global pandemic

due to the SARS-CoV-2 virus [

–

]. However, many operational, logistical and statistical challenges around

platform trials remain.

The deﬁnition of platform trials used in this article is that they are clinical trials which investigate multiple

treatments or treatment combinations in the context of a single disease, possibly within several sub-studies

for diﬀerent disease sub-types or targeting diﬀerent trial participant populations. In a platform trial, both

drugs or drug combinations within existing sub-studies, as well as new sub-studies, may enter or leave the

arXiv:2210.06228v2 [stat.AP] 11 Jan 2023

trial over time, allowing the trial to run inﬁnitely, in principle. Within each sub-study, many adaptive and

innovative design elements may be combined that clearly separate platform trials from more classical trial

designs [

]. For a more detailed introduction, we refer to Meyer et al.

[2]

, where we conducted a comprehensive

systematic search to review current literature on master protocol trials from a design and analysis perspective.

A compact glossary of common terms related to platform trials can be found in Table 1, while a more detailed

list of terms and explanations can be found online [12].

Platform trials can leverage their main strengths such as adaptive design elements, testing multiple hypothe-

ses in a single trial framework, reduced time to make decisions, ease of incorporating new investigational

treatments into the ongoing trial and possibilities for collaboration between diﬀerent consortia/sponsors.

In 2018, the Innovative Medicines Initiative (IMI) put forth a call for proposals for the development of

integrated research platforms to conduct platform trials to enable more patient-centric drug development.

A consortium of 36 private and public partners have come together in a strategic partnership to deliver on

the IMI proposal goals; the project is called EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) [

Among the expected outputs of the initiative are publicly available master protocol templates for platform

trials and four disease-speciﬁc master protocols for platform trials ready to operate in disease areas still

facing high unmet clinical need; one of those diseases being non-alcoholic steatohepatitis (NASH).

NASH is a more progressive form of non-alcoholic fatty liver disease (NAFLD) and is estimated to af-

fect approximately 5% of the world population. The disease is characterized by the accumulation of fat in

the liver in the absence of signiﬁcant alcohol intake or other secondary causes of hepatic steatosis [

Over time, chronic inﬂammation and liver cell injury lead to ﬁbrosis and eventually cirrhosis including

complications of end-stage liver disease and hepatocellular carcinoma. Indeed, NASH complications are

rapidly becoming the leading indication for liver transplantation. In addition, NASH is associated with

higher risks of developing cardiovascular diseases, which is the primary cause of death for most people

aﬀected. Currently, there are no approved treatments for NASH in the US and EU and in recent years several

compounds failed to meet their phase 3 primary endpoint(s) [

]. However, developing treatments for

NASH is a very active area of clinical research with dozens of industry-sponsored interventional studies active

or recruiting trial participants across phases 1 through 3 with the vast majority in phase 1 or 2 according to

ClinialTrials.gov and the EU clinical trials register (https://www.clinicaltrialsregister.eu).

To facilitate and accelerate the identiﬁcation of the most eﬀective and promising novel treatment op-

tions for trial participants with NASH, multiple potential novel therapies, as well as combinations of novel

mechanisms of action, will need to be evaluated in well-designed early clinical studies before advancing to

pivotal phase 3 programs. From a platform study perspective, Phase 2b is often the preferred trial design

as it generally oﬀers a robust pipeline for most indications and the ability to make decisions more rapidly

before committing to longer, more costly development. This is particularly true for NASH where there is

an abundance of compounds in early development and phase 3 programs tend to run over several years.

Importantly, there are broadly common design elements, study populations, procedures, and endpoints for

NASH phase 2b clinical studies which are aligned with Health Authority (HA) guidance.

Both the United States Food and Drug Administration (FDA) and the European Medicines Agency (EMA)

have put forward advice for developing drugs for patients with non-cirrhotic NASH [

–

]. Both HAs note

that the risk of progression to clinical outcomes (i.e., both liver-related and non liver-related morbidity and

mortality) is mainly related to ﬁbrosis stage. Therefore, the non-cirrhotic NASH population that should be

studied are individuals with either ﬁbrosis stage 2 (F2) or stage 3 (F3) since they are at increased risk of

progression relative to those with little (F1) or no (F0) liver ﬁbrosis [

–

]. In addition, the recognition by

the HAs that the length of time necessary to observe a suﬃcient number of clinical events to assess drug

eﬃcacy may hamper drug development has led the HAs to recommend improvement in liver histology as

clinical trial endpoints (i.e., resolution of steatohepatitis and no worsening of liver ﬁbrosis, improvement in

liver ﬁbrosis greater than or equal to one stage with no worsening of steatohepatitis), which can be used

as surrogates for approval in Phase 3 according to the accelerated approval pathways. Therefore, the FDA

guidance advises that phase 2b studies demonstrate eﬃcacy on a histological endpoint after at least 12-18

months of treatment, given that histological change takes an extended period of time to occur using a range

of doses to support phase 3 dose selection. Therefore, members of EU-PEARL are currently developing

a master protocol to support a phase 2b platform trial in NASH and this paper, as well as a previously

published simulation study [

], describe the initiative’s eﬀorts to simulate the performance of the parameters

used to make decisions on whether or not the treatment being evaluated is eﬀective.

Table 1: Glossary for important terms related to platform trials, taken partly from ICH E9 [

], partly

EU-PEARL D2.1 [12].

Term Description

Adaptive Design

An adaptive design allows the pre-speciﬁcation of ﬂexible

components to the major aspects of the trial, like the treatment

arms used (dose, frequency, duration, combinations, etc.), the

allocation to the diﬀerent treatment arms, the eligible patient

population, and the sample size. An adaptive design can learn

from the accruing data what the most therapeutic doses or arms

are, allowing for example, the design to home in on the best arms.

Integrated Research

Platform

An Integrated Research Platform (IRP) is a novel clinical

development concept centered on a master trial protocol which

can accommodate multi-sourced interventions using the existing

infrastructure of hospitals and federated patient data in design,

planning and execution, while an optimized regulatory pathway

for these novel treatments has been assured.

Master Protocol

The term “master protocol” refers to a single overarching design

developed to evaluate multiple hypothesis, and the general goals

are to improve eﬃciency and establish uniformity through

standardization of procedures in the development and evaluation

of diﬀerent interventions. Under a common infrastructure, the

master protocol may be diﬀerentiated into multiple parallel

sub-studies to include standardized trial operational structures,

patient recruitment and selection, data collection, analysis, and

management. In a platform trial the protocol will have the

infrastructure to drop interventions and allow new interventions

or combinations of interventions to enter the study based on

decision rules in the master protocol.

Platform Trials

Clinical trials which investigate multiple treatments or treatment

combinations in the context of a single disease, possibly within

several sub-studies for diﬀerent disease sub-types or targeting

diﬀerent trial participant populations. For more information, see

section 1.

Multi-center Trial

A clinical trial conducted according to a single protocol but at

more than one site, and therefore, carried out by more than one

investigator.

Frequentist Methods

Statistical methods, such as signiﬁcance tests and conﬁdence

intervals, which can be interpreted in terms of the frequency of

certain outcomes occurring in hypothetical repeated realisations

of the same experimental situation.

Bayesian Methods

Approaches to data analysis that provide a posterior probability

distribution for some parameter (e.g. treatment eﬀect), derived

from the observed data and a prior probability distribution for

the parameter. The posterior distribution is then used as the

basis for statistical inference.

Interim Analysis

Any analysis intended to compare treatment arms with respect to

eﬃcacy or safety at any time prior to the formal completion of a

trial.

2 Methods

2.1 Platform Design

An overview of the proposed platform trial design can be found in Figure 1. Generally, it is assumed that

after an initial inclusion of a certain number of cohorts each consisting of treatment and matching control,

further cohorts will enter over time while some of the existing cohorts might be discontinued for eﬃcacy or

futility. Trial participants entering the platform will be allocated between open cohorts. Within open cohorts,

trial participants will be equally allocated between control and treatment arm using a block randomization

of length two. Finally, the platform ends when all cohorts have ﬁnished their analyses. If the inclusion

and exclusion criteria of the diﬀerent cohorts are similar, it might be preferable to share the accumulating

information on the control treatments, at least for concurrently enrolling trial participants. While there

is a lot of controversy regarding the use of non-concurrent controls [

], sharing only information on trial

participants that could have been randomized to the arm under investigation seems uncontroversial (note that

this requires data to be concurrent). As noted before, platform trials can run perpetually without limiting the

number of drugs going into the trial. Any potentially successful compound in a NASH phase 2b trial would

have to show either resolution of NASH without worsening of ﬁbrosis (binary endpoint 1) and/or 1-stage

ﬁbrosis improvement without worsening of NASH (binary endpoint 2).

Endpoints 1 and 2 are correlated binary endpoints and clinical studies have demonstrated a strong link

between histologic resolution of steatohepatitis with improvement in ﬁbrosis [

], therefore, improvement in

endpoint 1 could lead to improvement in endpoint 2 but not necessarily the converse and not necessarily during

the same time frame. The current regulatory guidance is that the FDA recommends demonstrating endpoint

1 OR endpoint 2 and the EMA recommends demonstrating endpoint 1 AND endpoint 2 [

–

]. For this

simulation study, we decided to follow FDA endpoint recommendations. Within EU-PEARL, several possible

phase 2b platform trial designs for NASH were considered - treatment (one dose; could be monotherapy

or combination therapy) versus control, treatment (multiple doses; could be monotherapy or combination

therapy) versus control, combination therapy versus monotherapies versus control, etc. Furthermore, it was

considered whether the ﬁnal endpoints (which are observed after roughly 48-52 weeks) should be used for

interim decision making or whether a short-term surrogate endpoint should be used. Based on the proposed

design, comprehensive simulations were run for two scenarios: monotherapy (one dose) versus control and

combination therapy versus monotherapies versus control. We will present results of the former in this paper

and results of the latter can be found in Meyer et al. [24,29].

2.2 Decision Rules

Decisions on whether or not to promote treatments to the next stage of development can be based on diﬀerent

principles such as ﬁxed thresholds for treatment eﬀect estimates, the p-values of statistical frequentist tests

for treatment eﬃcacy, conditional or predictive probabilities of ﬁnal trial success. Many readers might be

familiar with group-sequential trials where early stopping for futility or eﬃcacy is based on the p-values from

statistical tests which are adjusted for repeated looks into the data, such as the O’Brien-Fleming test [

In some simple situations (e.g. if stopping the entire clinical trial for eﬃcacy or futility is the only permitted

interim decision option), it is possible to convert such decision rules into each other [

] (in the sense that

a decision rule given by a threshold on conditional power can equivalently be stated by a correspondingly

recalculated threshold on the estimated treatment eﬀect, say). In platform trials, however, the decision space

is usually more complicated and comprises interdependent decisions such as stopping arms without stopping

the entire trial or selecting treatments if they are suﬃciently superior to other treatments. In such situations,

there is no simple 1-to-1 correspondence between decision rules formulated on diﬀerent scales (e.g. a decision

rule which is inﬂuenced by the treatment eﬀect estimates from several treatments cannot be converted into a

Platform Design

Time

Cohort 1

Cohort 2

Cohort 3

Cohort 4

Cohort 5

Regimen 1

SoC

Regimen 2

Regimen 3

Regimen 4

Regimen 5

„Regimen“

could be e.g.

monotherapy,

combination

therapy, ...

Figure 1: Phase 2b platform trial design in non-alcoholic steatohepatitis (NASH). After an initial inclusion of

two cohorts consisting of control (usually the standard-of-care, ”SOC”) and ”regimen” arm (which could be a

monotherapy or a combination therapy), more cohorts of the same structure are entering the trial over time.

Within each cohort, several interim and a ﬁnal analysis are conducted using the co-primary binary endpoints

”NASH resolution without worsening of ﬁbrosis” and ”Fibrosis improvement without worsening of NASH”.

The platform trial ends when all cohorts have been evaluated.

ﬁxed threshold for one speciﬁc treatment). It is also very diﬃcult to provide decision rules on un-standardized

measures such as treatment eﬀect estimates, since these would have to be derived anew for every concrete

application. For these reasons, we focus on Bayesian posterior probabilities [

] as the main vehicle for making

decisions in this paper. The beneﬁt of using Bayesian decision rules is their ﬂexibility regarding extensions

to several criteria and interim analyses. To illustrate the basic mechanics, we introduce the concept for

comparing the response rate of a new treatment (

πE

) with the response rate of the Standard-of-Care (SoC)

(

πS

) in a clinical trial. For an analysis after observing data

, we are conducting a Bayesian analysis with the

aim of deciding whether there is enough evidence to declare the treatment eﬀective. First, we will introduce

the concept for a Bayesian decision rule testing a single endpoint using a parsimonious notation for illustrative

purposes. Later and in the appendix, we will show how the parameters could be speciﬁed for the endpoints

at hand in NASH. Diﬀerent levels of evidence will be introduced depending on the parameterization of the

Bayesian decision rule. Typically, a Bayesian decision rule of the following sort could be used for comparing

the new treatment to the control SoC (the priors on πSand πEare omitted for better readability):

Declare Eﬃcacy, if P(πE> πS+δ|D)> γ (1)

with some pre-speciﬁed probability threshold

and pre-deﬁned margin

for the targeted treatment eﬀect

of interest. Such a decision rule based on a posterior distribution can, but does not have to, correspond

to a particular null hypothesis (e.g.

πE> πS

). For example, it is sometimes appropriate to use

so-called ”shrinkage estimators” where the single treatment eﬀect estimates in a platform trial are ”shrunk”

towards a common average eﬀect. This is appropriate if drugs share a common mechanism of action and it is

therefore a priori plausible that they may have similar eﬀects. In such situations, the decision on a single

drug is inﬂuenced by the performance of the entire class of drugs. For better readability, the dependence on

the data Dis omitted in following sections.

Decision rules should provide a high level of conﬁdence that graduating compounds are competitive with

respect to the current landscape of compounds in development with publicly accessible phase 2/3 studies

published. In particular, semaglutide demonstrated a 42 percentage point response rate increase in NASH

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Designinganexploratoryphase2bplatformtrialinNASHwithcorrelated,co-primarybinaryendpointsEliasLaurinMeyer1,PeterMesenbrink2,NicholasA.DiProspero3,JuanM.Pericas4,5,EkkehardGlimm6,7,VladRatziu8,ElenaSena4,andFranzKonig1,*onbehalfoftheEU-PEARLNASHInvestigators+1CenterforMedicalDataScience,MedicalUnive...

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Designing an exploratory phase 2b platform trial in NASH with correlated co-primary binary endpoints Elias Laurin Meyer1 Peter Mesenbrink2 Nicholas A. Di Prospero3 Juan M. Peric as45

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