Power/sample size calculations for assessing correlates of risk in clinical efficacy trials

The quest for vaccine-induced immune correlates of protection against tuberculosis

Immunization strategies against tuberculosis (TB) that confer better protection than neonatal vaccination with the 101-year-old Bacille Calmette-Guerin (BCG) are urgently needed to control the epidemic, but clinical development is hampered by a lack of established immune correlates of protection (CoPs). Two phase 2b clinical trials offer the first opportunity to discover human CoPs against TB. Adolescent BCG re-vaccination showed partial protection against Mycobacterium tuberculosis (Mtb) infection, as measured by sustained IFNγ release assay (IGRA) conversion. Adult M72/AS01E vaccination showed partial protection against pulmonary TB. We describe two collaborative research programs to discover CoPs against TB and ensure rigorous, streamlined use of available samples, involving international immunology experts in TB and state-of-the-art technologies, sponsors and funders. Hypotheses covering immune responses thought to be important in protection against TB have been defined and prioritized. A statistical framework to integrate the data analysis strategy was developed. Exploratory analyses will be performed to generate novel hypotheses.

A unified approach to sample size and power determination for testing parameters in generalized linear and time-to-event regression models

To ensure that a study can properly address its research aims, the sample size and power must be determined appropriately. Covariate adjustment via regression modeling permits more precise estimation of the effect of a primary variable of interest at the expense of increased complexity in sample size/power calculation. The presence of correlation between the main variable and other covariates, commonly seen in observational studies and non-randomized clinical trials, further complicates this process. Though sample size and power specification methods have been obtained to accommodate specific covariate distributions and models, most existing approaches rely on either simple approximations lacking theoretical support or complex procedures that are difficult to apply at the design stage. The current literature lacks a general, coherent theory applicable to a broader class of regression models and covariate distributions. We introduce succinct formulas for sample size and power determination with the generalized linear, Cox, and Fine-Gray models that account for correlation between a main effect and other covariates. Extensive simulations demonstrate that this method produces studies that are appropriately sized to meet their type I error rate and power specifications, particularly offering accurate sample size/power estimation in the presence of correlated covariates.

Designing a Study of Correlates of Risk for Ebola Vaccination

The recombinant vesicular stomatitis virus (rVSV) Ebola vaccine was shown to be very efficacious in a novel ring vaccination trial in Guinea. However, no correlates of vaccine protection have been established for Ebola vaccines. Several Ebola vaccine candidates are available, but conducting randomized trials of additional candidates in outbreaks is difficult. Establishing correlates of vaccine protection is essential. Here we explore power and sample-size calculations to evaluate potential correlates of risk during an Ebola vaccination campaign in an outbreak. The method requires that a blood draw be made at a predetermined time after vaccination. The statistical analysis estimates the relative risk of the Ebola endpoint occurring from after the blood draw through to the end of follow-up, contrasting vaccine recipients with different values of the immune response marker. The analysis can be done assuming a trichotomous or continuous marker. Under certain assumptions, at an overall vaccine efficacy of 75%, 50 Ebola endpoints in the vaccinees provided good power. At an overall vaccine efficacy of 90%, 20 Ebola endpoints gave good power. Power was highest when more vaccinees were in the high- and low-responder groups versus the middle group and when vaccine efficacy differed the most between the high- and low-responder groups.

Landscapes of binding antibody and T-cell responses to pox-protein HIV vaccines in Thais and South Africans

BACKGROUND

HIV vaccine trials routinely measure multiple vaccine-elicited immune responses to compare regimens and study their potential associations with protection. Here we employ unsupervised learning tools facilitated by a bidirectional power transformation to explore the multivariate binding antibody and T-cell response patterns of immune responses elicited by two pox-protein HIV vaccine regimens. Both regimens utilized a recombinant canarypox vector (ALVAC-HIV) prime and a bivalent recombinant HIV-1 Envelope glycoprotein 120 subunit boost. We hypothesized that within each trial, there were participant subgroups sharing similar immune responses and that their frequencies differed across trials.

METHODS AND FINDINGS

We analyzed data from three trials-RV144 (NCT00223080), HVTN 097 (NCT02109354), and HVTN 100 (NCT02404311), the latter of which was pivotal in advancing the tested pox-protein HIV vaccine regimen to the HVTN 702 Phase 2b/3 efficacy trial. We found that bivariate CD4+ T-cell and anti-V1V2 IgG/IgG3 antibody response patterns were similar by age, sex-at-birth, and body mass index, but differed for the pox-protein clade AE/B alum-adjuvanted regimen studied in RV144 and HVTN 097 (PAE/B/alum) compared to the pox-protein clade C/C MF59-adjuvanted regimen studied in HVTN 100 (PC/MF59). Specifically, more PAE/B/alum recipients had low CD4+ T-cell and high anti-V1V2 IgG/IgG3 responses, and more PC/MF59 recipients had broad responses of both types. Analyses limited to "vaccine-matched" antigens suggested that some of the differences in responses between the regimens could have been due to antigens in the assays that did not match the vaccine immunogens. Our approach was also useful in identifying subgroups with unusually absent or high co-responses across assay types, flagging individuals for further characterization by functional assays. We also found that co-responses of anti-V1V2 IgG/IgG3 and CD4+ T cells had broad variability. As additional immune response assays are standardized and validated, we anticipate our framework will be increasingly valuable for multivariate analysis.

CONCLUSIONS

Our approach can be used to advance vaccine development objectives, including the characterization and comparison of candidate vaccine multivariate immune responses and improved design of studies to identify correlates of protection. For instance, results suggested that HVTN 702 will have adequate power to interrogate immune correlates involving anti-V1V2 IgG/IgG3 and CD4+ T-cell co-readouts, but will have lower power to study anti-gp120/gp140 IgG/IgG3 due to their lower dynamic ranges. The findings also generate hypotheses for future testing in experimental and computational analyses aimed at achieving a mechanistic understanding of vaccine-elicited immune response heterogeneity.

Generating Survival Times Using Cox Proportional Hazards Models with Cyclic and Piecewise Time-Varying Covariates

Time-to-event outcomes with cyclic time-varying covariates are frequently encountered in biomedical studies that involve multiple or repeated administrations of an intervention. In this paper, we propose approaches to generating event times for Cox proportional hazards models with both time-invariant covariates and a continuous cyclic and piecewise time-varying covariate. Values of the latter covariate change over time through cycles of interventions and its relationship with hazard differs before and after a threshold within each cycle. The simulations of data are based on inverting the cumulative hazard function and a log link function for relating the hazard function to the covariates. We consider closed-form derivations with the baseline hazard following the exponential, Weibull, or Gompertz distribution. We propose two simulation approaches: one based on simulating survival data under a single-dose regimen first before data are aggregated over multiple-dosing cycles and another based on simulating survival data directly under a multiple-dose regimen. We consider both fixed intervals and varying intervals of the drug administration schedule. The method's validity is assessed in simulation experiments. The results indicate that the proposed procedures perform well in generating data that conform to their cyclic nature and assumptions of the Cox proportional hazards model.

Basis and Statistical Design of the Passive HIV-1 Antibody Mediated Prevention (AMP) Test-of-Concept Efficacy Trials

BACKGROUND

Anti-HIV-1 broadly neutralizing antibodies (bnAbs) have been developed as potential agents for prevention of HIV-1 infection. The HIV Vaccine Trials Network and the HIV Prevention Trials Network are conducting the Antibody Mediated Prevention (AMP) trials to assess whether, and how, intravenous infusion of the anti-CD4 binding site bnAb, VRC01, prevents HIV-1 infection. These are the first test-of-concept studies to assess HIV-1 bnAb prevention efficacy in humans.

METHODS

The AMP trials are two parallel phase 2b HIV-1 prevention efficacy trials conducted in two cohorts: 2700 HIV-uninfected men and transgender persons who have sex with men in the United States, Peru, Brazil, and Switzerland; and 1500 HIV-uninfected sexually active women in seven countries in sub-Saharan Africa. Participants are randomized 1:1:1 to receive an intravenous infusion of 10 mg/kg VRC01, 30 mg/kg VRC01, or a control preparation every 8 weeks for a total of 10 infusions. Each trial is designed (1) to assess overall prevention efficacy (PE) pooled over the two VRC01 dose groups vs. control and (2) to assess VRC01 dose and laboratory markers as correlates of protection (CoPs) against overall and genotype- and phenotype-specific infection.

RESULTS

Each AMP trial is designed to have 90% power to detect PE > 0% if PE is ≥ 60%. The AMP trials are also designed to identify VRC01 properties (i.e., concentration and effector functions) that correlate with protection and to provide insight into mechanistic CoPs. CoPs are assessed using data from breakthrough HIV-1 infections, including genetic sequences and sensitivities to VRC01-mediated neutralization and Fc effector functions.

CONCLUSIONS

The AMP trials test whether VRC01 can prevent HIV-1 infection in two study populations. If affirmative, they will provide information for estimating the optimal dosage of VRC01 (or subsequent derivatives) and identify threshold levels of neutralization and Fc effector functions associated with high-level protection, setting a benchmark for future vaccine evaluation and constituting a bridge to other bnAb approaches for HIV-1 prevention.