Statistical considerations for the Nipah virus treatment study Lori E Dodd, PhD Clinical Trials Research Section Division of Clinical Research/NIAID

Why randomize in outbreak setting? Evaluating potential efficacy of experimental treatment requires a comparable control group Ensuring a comparable control group is especially tricky in an emerging infectious disease: Risk factors for death not well known Supportive care may differ across sites & outbreaks Randomization will tend to balance risk factors & supportive care differences It is possible treatment may cause harm Randomized control group an efficient way to identify this.

Ebola 2013-2016 treatment studies 8 studies reported evaluations of candidate treatments: Amodiaquine, Convalescent Plasma, Convalescent Whole Blood, Favipiravir (2 studies), Interferon β-1⍺, TKM-13083, ZMappTM Only ZMappTM used a randomized-controlled design All other studies used historical controls What can we learn from comparing the control groups?

Mortality rates vary between 30% and 66%. Potential reasons for variability: Different amounts of virus at time of infection? Different time from onset of symptoms to treatment? Virus variants? Age differences? Gender differences? Genetics? Differences in supportive care? Mortality rates from 6 control groups: Adjustments for cycle threshold, age, and gender do not eliminate the between-study heterogeneity

Favipiravir Study Is this a reasonable estimate of the benefit of favipiravir?

Adjusting for baseline covariates does not resolve this predicament.* Randomization will tend to balance the differences in risk of death of baseline variables. Favipiravir Study This control group would suggest harm.

TKM-13083 Study Harm?

TKM-13083 Concern about TKM does not change even after adjusting for CT, age and gender.

Randomization will not A lead physician on ZMappTM regrets not having a placebo: ZMappTM was perceived to be a “magic serum.” Perception may have influenced patient management. Patients require continual monitoring during ZMappTM infusion. Potential differences in background care??? Balance post-baseline differences! Differences between groups related to treatment assignment AFTER randomization may still bias comparisons. Examples of unintended post-randomization bias: --Patients receiving experimental therapy are monitored more frequently, receiving improved background care, OR --Patients receiving control therapy are monitored more frequently, receiving improved background care Placebo-controlled trials address this potential bias.

Statistical analysis for this RCT Primary Endpoint: 28-day mortality Statistical test: test of lower mortality rate (using Boschloo’s test), two-sided type I error rate of 0.05 Sample size: 100 per arm Highly unlikely that one outbreak will provide sufficient statistical power for conclusive evidence Mortality rate Power for various sample sizes per arm SOC m102.4 50 75 100 80% 64% 40% 58% 71% 56% 72% 89% 96% 70% 49% 57% 75% 86% 50% 30% 53% 83% 20% 76% 87%

Draft statistical analysis plan: Action item Draft statistical analysis plan: who and what?

Randomization Stratified for statistical efficiency Limited to one stratification variable: Presence of neurologic symptoms (yes/no). Double-blinded design requires Process to ensure masking of treatment assignment Randomization via database with blinded treatment codes Pharmacist standardized operating procedure to preserve the blind Reporting of AEs to Data and Safety Monitoring Board who should be unblinded to treatment assignment

Action items Randomization SOP: list generation and database process SOP to ensure double-blinding SOP for reporting results to DSMB

Interim monitoring considerations Early data are unreliable but ethical considerations demand that a trial stops for Definitive early efficacy or Definitive early harm Monitoring too frequently increases chances of a false positive finding Study likely to continue across multiple outbreaks Solution: group sequential monitoring

Definitive early efficacy Truncated O’Brien-Fleming boundary with 5 interim looks: Number total 24 50 100 150 200 One-sided p-value boundary 0.0005 0.009 0.021 Mortality boundaries for first (n=24) interim analysis: Deaths in m102.4 Deaths in SOC 8+ 1 9+ 2 11+ 3 12 Flexibility to evaluate at the end of an outbreak

Definitive early harm Same frequency as for efficacy. Less evidence required to establish harm. p<0.05 for each look Mortality boundaries for first safety analysis (at n=24): Deaths in m102.4 Deaths in SOC 4+ 6+ 1 7+ 2 9+ 3

Conditional Power Method of calculating likelihood of concluding statistical significance given accumulated (but not complete) data Given the accumulated observed data (up to time of analysis), compute the probability of achieving statistical significance assuming the remaining data (up to total of 100/arm) follow assumed alternative: *e.g., 50% reduction in mortality Evaluated during interim analyses and/or accrual pauses (due to end of outbreak) —If conditional power < 20%, continuing study may be futile

Sample size reassessment After 100 participants have been accrued, sample size may be increased if: Conditional power is greater than 50%, or Pooled mortality rate is low

Continuing study across outbreaks Study continues (across outbreaks) without release of results until accrual completed or DSMB recommends early stopping. Procedures/agreements needed to ensure all sites/countries understand the importance of this. Early release of data without a definitive conclusion may make continued study of m102.4 impractical.

Prevail II example Randomized controlled trial comparing ZMappTM to standard-of-care during the 2013-16 Ebola outbreak Powered for a 50% reduction in mortality from 40% to 20% -200 subjects needed By January 2016, epidemic was clearly ending. Should study continue? West African Ebola outbreak was unprecedented. No pattern of regional outbreaks Unclear if and when another outbreak would occur

DSMB presented with the following data The PREVAIL II Writing Group. N Engl J Med 2016;375:1448-1456.

Recommendation DSMB recommended study closure and release of data Results published NEJM in 2016 ZMappTM was promising but results not definitive Ongoing debate about its efficacy Questions for thought: Should DSMB have kept study open? Suppose another outbreak was expected within a year, would DSMB have kept study going?

Action items DSMB charter DSMB approves protocol and monitoring plans Country-/site-wide agreement with monitoring plans Statistical analysis plan to describe interim analyses in detail