Not all Platform Trials are
Created Equal:

lessons from clinical trials in outbreak settings
Lori E Dodd, PhD
Clinical Trials Research Section
Biostatistics Research Branch
NIAID
Principal statistician for the PALM1 Ebola RCT and the ACTT COVID-19 trials

Platform Trials“Answer more questions, more efficiently and in less time†”

Shared resources allow for
Common control group• Sample size efficiency

Larger study -> greater statistical power-> more data on subgroups
Efficient use of study related procedures:• Screening & enrollment procedures• Sites• Systems• Data and safety monitoring board• Master protocol with appendices for added arms
† Woodcock and LaVange, NEJM, 2017

Platform Trial Decisions• Number of arms to include• Adjustments for multiple comparisons• When and how to add arms?
• When and how to drop arms?
• Defining the control group• Trade-off between quantity and quality

Number of study agents/arms• Number of candidate interventions• Candidates may differ in terms of pre-existing evidence of potential efficacy• More study arms means fewer participants in any given arm• Number of expected participants, often unknown• Target effect size, study power and type I error rate considerations drive sample size
• If number of participants could be known from the outset, one could optimize study design• Duration of study• Trade-off between obtaining an answer to one questions sooner versus many answers laterExample: PALM1 Ebola RCT in the DRC
Aug 14, 2018: protocol concept~80 cases reported
10th DRC outbreakPrevious outbreaks ended with anaverage of 133 cases (range 1 – 318)
Potential enrollment numbers uncertain
Candidate treatments:
ZMapp – monoclonal antibody studied inWest Africa outbreak (control)mAb114 – new monoclonal antibody
REGN-EB3 – new monoclonal antibody
Remdesivir – antiviral

How many arms can we study?

Type I Error Rate Control

Multiplicity issues must be addressed
Multiple hypothesis tests will inflate the overall Type I error rate
• You get “multiple bites at the apple”• Different drugs from different companies• Valuable to have different drugs evaluated in the same study• Strict multiplicity adjustments may disincentivize company participation• Some argue multiple comparison adjustments are not necessary but itdepends.
• Same drug but multiple arms are different doses => more strict adjustment• Dunnett’s test for comparisons to a common control => too strict?

• Platform trial power advantage remains: “When the total number of subjects is the samein a single versus separate experiments, power is generally higher in a single experimenteven if a Dunnett adjustment is made,” Follmann and Proschan, 1994
• Recommendation to enroll more controls than exp’l arms
• Strategy will reduce power for comparisons of exp’l arms, which is often of interest

PALM1 multiple comparison adjustments:• Product developers have little incentive to participate in a platform trial thatadjusts for multiple comparisons• With 2 primary comparisons: Dunnett’s pretty extreme/similar to a
Bonferroni adjustment• In Ebola setting, achievable sample size is unpredictable and limited• Increase in type I error rate beyond traditional/arbitrary 0.05 acceptable inthis setting – examples in rare cancers apply• Consider each comparison as a separate study• No adjustment for multiple comparisons planned for PALM1• US FDA concurred with this approach for this setting• Not applicable to all settings.
• Each setting must be considered separately

Adding Arms to a PLATFORM trial

Adding arms to an ongoing trial
How will addition of a study arm influence the existing arm
comparisons?
• For a fixed total sample size, power will be reduced• Delayed conclusion about efficacy of Arm B and Arm C• Power for the new arm will lag relative to those already under study

Possible explanatory variables:• Differences in supportive care
• Different assay platforms• Treatment facility differences• Viral load at time infection• Time from onset to enrollment• Co-morbidities• Host genetics
• Age
• Sex
• Changes over time of the above variables• Viral evolution

Importance of concurrent controls• Outbreak diseases may have considerable heterogeneity in natural
course of disease over time
• Standard of care changes rapidly and may dramatically alter “usualcare” outcomes (e.g. Ebola outbreak in 2014-2016)†• Study arms need equivalent patient populations (on average) so thatdifferences between arms can be attributed to exp’l treatment• Validity relies on concurrent controls– differences between arms areapproximately the same over time (for population)• Designs that do not keep an equal allocation of controls over time
may lead to errant conclusions†Dodd et al, Sci Trans Med, 2019, DOI: 10.1126/scitranslmed.aaw1049

Randomization strategies when adding arms
Response adaptive randomization (RAR)

Kristian Thorlund et al. BMJ 2018;360:bmj.k698• With RAR, randomization probabilities are updated based on outcomes, typically
restricted to “burn-in” periods set by a certain number enrolled.
• Adding an arm: “burn in” period designated for new arm• Existing data and pre-specified functions guide updates to randomization probabilities• Various approaches exist: Ventz et al (Biostatistics, 2018), Berry et al (Clin Trials, 2016)• Concern over time trends bias

Example: Adding an arm in PALM1
On November 20, 2018 enrollment started• Increasing pressure to add REGN-EB3, a triple monoclonal
antibody treatment.
On January 26, 2019 REGN-EB3 was added• At the time there were 51 enrolled, 15 of them were on
the control arm• 15 fewer controls for the REGN-EB3 to control comparison
not considered enough to raise concerns about power• Randomization allocation set to 1:1:1:1• What would we have done if there had been 30 controls?
50 controls?

Dropping Arms from a Platform Trial

When to drop arms?
What if Arm B crosses an efficacy boundary but Arm C and D have not?
• Stopping the control arm will prevent accumulation of additional
information about the efficacy of Arms C & D.
• Is it ethical to continue randomization to the control arm?
• Does it matter if the Arm D is close to crossing an efficacy boundary?
• Should the recommendation depend on the importance of themedical community having more than one treatment option?

PALM1: Interim Monitoring• Mid-July: learn of rumors that one therapeutic candidate is harmful• Increased concern amongst field team• August 9th meeting: 681 enrolled• Only 376 patients with a day 28 outcome in time for DSMB report• ~50% of total information, enrollment 94% complete• Study team, including site staff, well aware of this disparity andconcerned. The study statistician was concerned too!
• Looking at earlier timepoint (e.g., day 10) not considered in protocol

What we (the study team) didn’t know
• Fewer than 5% of deaths occurred after day 10.
• Early on the DSMB requested analysis on a “surrogate” endpoint• For participants who have not reached day 28 but have been on study at least
10 days, include their most current status.
• Compare mortality proportions based on this surrogate

PALM1 Aug 7, 2019 DSMB meeting• Using 10-day mortality, a near-perfect surrogate for 28-day mortality,interim results showed that the two leading study arms (mAb114 andREGN-EB3) would be statistically significant after the final 28-day
outcome data were available.
• The DSMB recommended stopping random assignment to the control
arm and remdesivir and continuing follow-up to 28 days for all
patients randomly assigned by August 7, 2019
• The sponsor (the National Institute of Allergy and Infectious Diseases)
accepted the recommendation and transitioned new patients to theproven treatments.
Mulangu et al, NEJM 2019
Dodd & Proschan, NEJM Evidence 2022

Is there value in having more than one trial ?
• Yes• Additional trial(s) add external validity• Regulatory requirement for two adequate and well-controlled clinicalinvestigations (see FDA guidance)†• Data in other settings/populations of value• Additional data may contribute to knowledge about subgroups (viameta-analysis)• Value depends on the quality of the other trials and the extent towhich they are harmonized† https://www.fda.gov/media/133660/download

Larger/Simple vs Smaller/Complex Trials“Large and simple” does not necessarily mean better

Larger & simple
Example: Solidarity

Smaller & Complex
Example: ACTT-1+ Large sample sizes/more power-

Smaller studies/less power+ Less burden on site staff per participant-More burden on site staff and study team/participant-

Less likely to be placebo-controlled+

More likely to be placebo-controlled- Primary endpoint: in-hospital mortality-

Primary endpoint: time to discharge- Less likely to be sufficient for FDA approvals+

More likely to be sufficient for FDA approvals+

More safety data; ability to assess signals of harm, stoparms, protect participants+More data on secondary endpoints: evidence about
treatment efficacy comes from much more than theprimary endpoint+

More granular data can inform other analyses, studies,
exploration of mechanism of action+More data quality checking/monitoring; higher quality
studies-Little/less safety data

- Less data on secondary endpoints- Less granular data; limited data targeted on primary
endpoint. Data collection at fewer time pointsSize limits ability quality oversight- Less data quality checking/monitoring overall

Conclusions• COVID-19 has provided an unprecedented number of clinical trials andparticipants over a relatively short duration.
• Trials should seek to balance the advantages of a large/simple trial with thebenefits of smaller/complex trials.
• Response adaptive randomization may lead to inflated type I error andstatistical inefficiency-> avoid for definitive clinical trials• Definitive results about any arm will effect ability to collect data on otherarms.
• Adding an arm during a study presents challenges• Trade-off in statistical power with many arms
• Answer one question faster?

• Multiplicity adjustments need careful consideration for each setting