Interval between prior SARS-CoV-2 infection and booster vaccination impacts magnitude and quality of antibody and B cell responses.

SARS-CoV-2 mRNA booster vaccines provide protection from severe disease, eliciting strong immunity that is further boosted by previous infection. However, it is unclear whether these immune responses are affected by the interval between infection and vaccination. Over a 2-month period, we evaluated antibody and B cell responses to a third-dose mRNA vaccine in 66 individuals with different infection histories. Uninfected and post-boost but not previously infected individuals mounted robust ancestral and variant spike-binding and neutralizing antibodies and memory B cells. Spike-specific B cell responses from recent infection (<180 days) were elevated at pre-boost but comparatively less so at 60 days post-boost compared with uninfected individuals, and these differences were linked to baseline frequencies of CD27lo B cells. Day 60 to baseline ratio of BCR signaling measured by phosphorylation of Syk was inversely correlated to days between infection and vaccination. Thus, B cell responses to booster vaccines are impeded by recent infection.

Combination anti-HIV antibodies provide sustained virological suppression.

Antiretroviral therapy is highly effective in suppressing human immunodeficiency virus (HIV)1. However, eradication of the virus in individuals with HIV has not been possible to date2. Given that HIV suppression requires life-long antiretroviral therapy, predominantly on a daily basis, there is a need to develop clinically effective alternatives that use long-acting antiviral agents to inhibit viral replication3. Here we report the results of a two-component clinical trial involving the passive transfer of two HIV-specific broadly neutralizing monoclonal antibodies, 3BNC117 and 10-1074. The first component was a randomized, double-blind, placebo-controlled trial that enrolled participants who initiated antiretroviral therapy during the acute/early phase of HIV infection. The second component was an open-label single-arm trial that enrolled individuals with viraemic control who were naive to antiretroviral therapy. Up to 8 infusions of 3BNC117 and 10-1074, administered over a period of 24 weeks, were well tolerated without any serious adverse events related to the infusions. Compared with the placebo, the combination broadly neutralizing monoclonal antibodies maintained complete suppression of plasma viraemia (for up to 43 weeks) after analytical treatment interruption, provided that no antibody-resistant HIV was detected at the baseline in the study participants. Similarly, potent HIV suppression was seen in the antiretroviral-therapy-naive study participants with viraemia carrying sensitive virus at the baseline. Our data demonstrate that combination therapy with broadly neutralizing monoclonal antibodies can provide long-term virological suppression without antiretroviral therapy in individuals with HIV, and our experience offers guidance for future clinical trials involving next-generation antibodies with long half-lives.

Changing interim monitoring in response to internal clinical trial data.

Designing clinical trials for emerging infectious diseases such as COVID-19 is challenging because information needed for proper planning may be lacking. Pre-specified adaptive designs can be attractive options, but what happens if a trial with no such design needs to be modified? For example, unexpectedly high efficacy (approximately 95%) in two COVID-19 vaccine trials might cause investigators in other COVID-19 vaccine trials to increase the number of interim analyses to allow earlier stopping for efficacy. If such a decision is based solely on external data, there are no issues, but what if internal trial data by arm are also examined? Fortunately, the conditional error principle of Müller and Schäfer (2004) can be used to ensure no inflation of the type 1 error rate, even if no interim analyses were planned. We study the properties, including limitations, of this method. We provide a shiny app to evaluate changes in timing of interim analyses in response to outcome data by arm in clinical trials.

Does Remdesivir Lower COVID-19 Mortality? A Subgroup Analysis of Hospitalized Adults Receiving Supplemental Oxygen.

The first Adaptive COVID-19 Treatment Trial (ACTT-1) showed that remdesivir improved COVID-19 recovery time compared with placebo in hospitalized adults. The secondary outcome of mortality was almost significant overall (p = 0.07) and highly significant for people receiving supplemental oxygen at enrollment (p = 0.002), suggesting a mortality benefit concentrated in this group. We explore analysis methods that are helpful when a single subgroup benefits from treatment and apply them to ACTT-1, using baseline oxygen use to define subgroups. We consider two questions: (1) is the remdesivir effect for people receiving supplemental oxygen real, and (2) does this effect differ from the overall effect? For Question 1, we apply a Bonferroni adjustment to subgroup-specific hypothesis tests and the Westfall and Young permutation test, which is valid when small cell counts preclude normally distributed test statistics (a frequently unexamined condition in subgroup analyses). For Question 2, we introduce Qmax, the largest standardized difference between subgroup-specific effects and the overall effect. Qmax simultaneously tests whether any subgroup effect differs from the overall effect and identifies the subgroup benefitting most. We demonstrate that Qmax strongly controls the familywise error rate (FWER) when test statistics are normally distributed with no mean-variance relationship. We compare Qmax to a related permutation test, SEAMOS, which was previously proposed but not extensively applied or tested. We show that SEAMOS can have inflated Type 1 error under the global null when control arm event rates differ between subgroups. Our results support a mortality benefit from remdesivir in people receiving supplemental oxygen.

Comparison of Bayesian and frequentist monitoring boundaries motivated by the Multiplatform Randomized Clinical Trial.

BACKGROUND: The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary. METHODS: The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline. RESULTS: A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary. CONCLUSIONS: In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.

A note on familywise error rate for a primary and secondary endpoint.

Hung et al. (2007) considered the problem of controlling the type I error rate for a primary and secondary endpoint in a clinical trial using a gatekeeping approach in which the secondary endpoint is tested only if the primary endpoint crosses its monitoring boundary. They considered a two-look trial and showed by simulation that the naive method of testing the secondary endpoint at full level α at the time the primary endpoint reaches statistical significance does not control the familywise error rate at level α. Tamhane et al. (2010) derived analytic expressions for familywise error rate and power and confirmed the inflated error rate of the naive approach. Nonetheless, many people mistakenly believe that the closure principle can be used to prove that the naive procedure controls the familywise error rate. The purpose of this note is to explain in greater detail why there is a problem with the naive approach and show that the degree of alpha inflation can be as high as that of unadjusted monitoring of a single endpoint.

Discussion on "Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment for binary, ordinal, and time-to-event outcomes".

Benkeser et al. present a very informative paper evaluating the efficiency gains of covariate adjustment in settings with binary, ordinal, and time-to-event outcomes. The adjustment method focuses on estimating the marginal treatment effect averaged over the covariate distribution in both arms combined. The authors show that covariate adjustment can achieve power gains that could find answers more quickly. The suggested approach is an important weapon in the armamentarium against epidemics like COVID-19. I recommend evaluating the procedure against more traditional approaches for conditional analyses (e.g., logistic regression) and against blinded methods of building prediction models followed by randomization-based inference.

A primer on strong vs weak control of familywise error rate.

Multiple comparison adjustments have a long history, yet confusion remains about which procedures control type 1 error rate in a strong sense and how to show this. Part of the confusion stems from a powerful technique called the closed testing principle, whose statement is deceptively simple, but is sometimes misinterpreted. This primer presents a straightforward way to think about multiplicity adjustment.

A Randomized, Controlled Trial of ZMapp for Ebola Virus Infection.

BACKGROUND: Data from studies in nonhuman primates suggest that the triple monoclonal antibody cocktail ZMapp is a promising immune-based treatment for Ebola virus disease (EVD). METHODS: Beginning in March 2015, we conducted a randomized, controlled trial of ZMapp plus the current standard of care as compared with the current standard of care alone in patients with EVD that was diagnosed in West Africa by polymerase-chain-reaction (PCR) assay. Eligible patients of any age were randomly assigned in a 1:1 ratio to receive either the current standard of care or the current standard of care plus three intravenous infusions of ZMapp (50 mg per kilogram of body weight, administered every third day). Patients were stratified according to baseline PCR cycle-threshold value for the virus (≤22 vs. >22) and country of enrollment. Oral favipiravir was part of the current standard of care in Guinea. The primary end point was mortality at 28 days. RESULTS: A total of 72 patients were enrolled at sites in Liberia, Sierra Leone, Guinea, and the United States. Of the 71 patients who could be evaluated, 21 died, representing an overall case fatality rate of 30%. Death occurred in 13 of 35 patients (37%) who received the current standard of care alone and in 8 of 36 patients (22%) who received the current standard of care plus ZMapp. The observed posterior probability that ZMapp plus the current standard of care was superior to the current standard of care alone was 91.2%, falling short of the prespecified threshold of 97.5%. Frequentist analyses yielded similar results (absolute difference in mortality with ZMapp, -15 percentage points; 95% confidence interval, -36 to 7). Baseline viral load was strongly predictive of both mortality and duration of hospitalization in all age groups. CONCLUSIONS: In this randomized, controlled trial of a putative therapeutic agent for EVD, although the estimated effect of ZMapp appeared to be beneficial, the result did not meet the prespecified statistical threshold for efficacy. (Funded by the National Institute of Allergy and Infectious Diseases and others; PREVAIL II ClinicalTrials.gov number, NCT02363322 .).

Effect of HIV Antibody VRC01 on Viral Rebound after Treatment Interruption.

BACKGROUND: The discovery of potent and broadly neutralizing antibodies (bNAbs) against human immunodeficiency virus (HIV) has made passive immunization a potential strategy for the prevention and treatment of HIV infection. We sought to determine whether passive administration of VRC01, a bNAb targeting the HIV CD4-binding site, can safely prevent or delay plasma viral rebound after the discontinuation of antiretroviral therapy (ART). METHODS: We conducted two open-label trials (AIDS Clinical Trials Group [ACTG] A5340 and National Institutes of Health [NIH] 15-I-0140) of the safety, side-effect profile, pharmacokinetic properties, and antiviral activity of VRC01 in persons with HIV infection who were undergoing interruption of ART. RESULTS: A total of 24 participants were enrolled, and one serious alcohol-related adverse event occurred. Viral rebound occurred despite plasma VRC01 concentrations greater than 50 µg per milliliter. The median time to rebound was 4 weeks in the A5340 trial and 5.6 weeks in the NIH trial. Study participants were more likely than historical controls to have viral suppression at week 4 (38% vs. 13%, P=0.04 by a two-sided Fisher's exact test in the A5340 trial; and 80% vs. 13%, P<0.001 by a two-sided Fisher's exact test in the NIH trial) but the difference was not significant at week 8. Analyses of virus populations before ART as well as before and after ART interruption showed that VRC01 exerted pressure on rebounding virus, resulting in restriction of recrudescent viruses and selection for preexisting and emerging antibody neutralization-resistant virus. CONCLUSIONS: VRC01 slightly delayed plasma viral rebound in the trial participants, as compared with historical controls, but it did not maintain viral suppression by week 8. In the small number of participants enrolled in these trials, no safety concerns were identified with passive immunization with a single bNAb (VRC01). (Funded by the National Institute of Allergy and Infectious Diseases and others; ACTG A5340 and NIH 15-I-0140 ClinicalTrials.gov numbers, NCT02463227 and NCT02471326 .).

Re-randomization tests in clinical trials.

As randomization methods use more information in more complex ways to assign patients to treatments, analysis of the resulting data becomes challenging. The treatment assignment vector and outcome vector become correlated whenever randomization probabilities depend on data correlated with outcomes. One straightforward analysis method is a re-randomization test that fixes outcome data and creates a reference distribution for the test statistic by repeatedly re-randomizing according to the same randomization method used in the trial. This article reviews re-randomization tests, especially in nonstandard settings like covariate-adaptive and response-adaptive randomization. We show that re-randomization tests provide valid inference in a wide range of settings. Nonetheless, there are simple examples demonstrating limitations.

Development process of a consensus-driven CONSORT extension for randomised trials using an adaptive design.

BACKGROUND: Adequate reporting of adaptive designs (ADs) maximises their potential benefits in the conduct of clinical trials. Transparent reporting can help address some obstacles and concerns relating to the use of ADs. Currently, there are deficiencies in the reporting of AD trials. To overcome this, we have developed a consensus-driven extension to the CONSORT statement for randomised trials using an AD. This paper describes the processes and methods used to develop this extension rather than detailed explanation of the guideline. METHODS: We developed the guideline in seven overlapping stages: 1) Building on prior research to inform the need for a guideline; 2) A scoping literature review to inform future stages; 3) Drafting the first checklist version involving an External Expert Panel; 4) A two-round Delphi process involving international, multidisciplinary, and cross-sector key stakeholders; 5) A consensus meeting to advise which reporting items to retain through voting, and to discuss the structure of what to include in the supporting explanation and elaboration (E&E) document; 6) Refining and finalising the checklist; and 7) Writing-up and dissemination of the E&E document. The CONSORT Executive Group oversaw the entire development process. RESULTS: Delphi survey response rates were 94/143 (66%), 114/156 (73%), and 79/143 (55%) in rounds 1, 2, and across both rounds, respectively. Twenty-seven delegates from Europe, the USA, and Asia attended the consensus meeting. The main checklist has seven new and nine modified items and six unchanged items with expanded E&E text to clarify further considerations for ADs. The abstract checklist has one new and one modified item together with an unchanged item with expanded E&E text. The E&E document will describe the scope of the guideline, the definition of an AD, and some types of ADs and trial adaptations and explain each reporting item in detail including case studies. CONCLUSIONS: We hope that making the development processes, methods, and all supporting information that aided decision-making transparent will enhance the acceptability and quick uptake of the guideline. This will also help other groups when developing similar CONSORT extensions. The guideline is applicable to all randomised trials with an AD and contains minimum reporting requirements.

Re-randomization tests for unplanned changes in clinical trials.

Despite the best efforts of investigators, problems forcing design changes can occur in clinical trials. Changes are usually relatively minor, but sometimes not. The primary endpoint or analysis may need to be revised, for example. It is common to regard any conclusion from such a tarnished trial as hypothesis-generating rather than definitive. This article reviews a very useful technique, re-randomization tests, for dealing with such anomalies. Re-randomization tests remain valid for testing a strong null hypothesis that treatment has no effect on the data that led to design changes. Another way of expressing this is that the data used to inform a design change must give no information about the treatment labels. This restriction has implications for limiting the amount of information examined by a committee deciding whether to make design alterations. While nothing can eliminate the pall cast by breaches of protocol, re-randomization tests following blinded and limited data examination go a long way toward amelioration.

Controlling the family-wise error rate in multi-arm, multi-stage trials.

BACKGROUND AND AIMS: Multi-arm, multi-stage trials have recently gained attention as a means to improve the efficiency of the clinical trials process. Many designs have been proposed, but few explicitly consider the inherent issue of multiplicity and the associated type I error rate inflation. It is our aim to propose a straightforward design that controls family-wise error rate while still providing improved efficiency. METHODS: In this article, we provide an analytical method for calculating the family-wise error rate for a multi-arm, multi-stage trial and highlight the potential for considerable error rate inflation in uncontrolled designs. We propose a simple method to control the error rate that also allows for computation of power and expected sample size. RESULTS: Family-wise error rate can be controlled in a variety of multi-arm, mutli-stage trial designs using our method. Additionally, our design can substantially decrease the expected sample size of a study while maintaining adequate power. CONCLUSION: Multi-arm, multi-stage designs have the potential to reduce the time and other resources spent on clinical trials. Our relatively simple design allows this to be achieved while weakly controlling family-wise error rate and without sacrificing much power.

Design of a Randomized Controlled Trial for Ebola Virus Disease Medical Countermeasures: PREVAIL II, the Ebola MCM Study.

BACKGROUND: Unique challenges posed by emerging infectious diseases often expose inadequacies in the conventional phased investigational therapeutic development paradigm. The recent Ebola outbreak in West Africa presents a critical case-study highlighting barriers to faster development. During the outbreak, clinical trials were implemented with unprecedented speed. Yet, in most cases, this fast-tracked approach proved too slow for the rapidly evolving epidemic. Controversy abounded as to the most appropriate study designs to yield safety and efficacy data, potentially causing delays in pivotal studies. Preparation for research during future outbreaks may require acceptance of a paradigm that circumvents, accelerates, or reorders traditional phases, without losing sight of the traditional benchmarks by which drug candidates must be assessed for activity, safety and efficacy. METHODS: We present the design of an adaptive, parent protocol, ongoing in West Africa until January 2016. The exigent circumstances of the outbreak and limited prior clinical experience with experimental treatments, led to more direct bridging from preclinical studies to human trials than the conventional paradigm would typically have sanctioned, and required considerable design flexibility. RESULTS: Preliminary evaluation of the "barely Bayesian" design was provided through computer simulation studies. The understanding and public discussion of the study design will help its future implementation.

Statistical considerations for a trial of Ebola virus disease therapeutics.

The 2014 West African outbreak of Ebola virus ravaged Liberia, Sierra Leone, and Guinea, causing hemorrhagic fever and death. The need to identify effective therapeutics was acute. The usual drug development paradigm of phase I, followed by phase II, and then phase III trials would take too long. These and other factors led to the design of a clinical trial of Ebola virus disease therapeutics that differs from more conventional clinical trial designs. This article describes the Ebola virus disease medical countermeasures trial design and the thinking behind it.

Combining one-sample confidence procedures for inference in the two-sample case.

We present a simple general method for combining two one-sample confidence procedures to obtain inferences in the two-sample problem. Some applications give striking connections to established methods; for example, combining exact binomial confidence procedures gives new confidence intervals on the difference or ratio of proportions that match inferences using Fisher's exact test, and numeric studies show the associated confidence intervals bound the type I error rate. Combining exact one-sample Poisson confidence procedures recreates standard confidence intervals on the ratio, and introduces new ones for the difference. Combining confidence procedures associated with one-sample t-tests recreates the Behrens-Fisher intervals. Other applications provide new confidence intervals with fewer assumptions than previously needed. For example, the method creates new confidence intervals on the difference in medians that do not require shift and continuity assumptions. We create a new confidence interval for the difference between two survival distributions at a fixed time point when there is independent censoring by combining the recently developed beta product confidence procedure for each single sample. The resulting interval is designed to guarantee coverage regardless of sample size or censoring distribution, and produces equivalent inferences to Fisher's exact test when there is no censoring. We show theoretically that when combining intervals asymptotically equivalent to normal intervals, our method has asymptotically accurate coverage. Importantly, all situations studied suggest guaranteed nominal coverage for our new interval whenever the original confidence procedures themselves guarantee coverage.