Augmented designs to assess immune response in vaccine trials

Abstract

This article introduces methods for use in vaccine clinical trials to help determine whether the immune response to a vaccine is actually causing a reduction in the infection rate. This is not easy because immune response to the (say HIV) vaccine is only observed in the HIV vaccine arm. If we knew what the HIV-specific immune response in placebo recipients would have been, had they been vaccinated, this immune response could be treated essentially like a baseline covariate and an interaction with treatment could be evaluated. Relatedly, the rate of infection by this baseline covariate could be compared between the two groups and a causative role of immune response would be supported if infection risk decreased with increasing HIV immune response only in the vaccine group. We introduce two methods for inferring this HIV-specific immune response. The first involves vaccinating everyone before baseline with an irrelevant vaccine, for example, rabies. Randomization ensures that the relationship between the immune responses to the rabies and HIV vaccines observed in the vaccine group is the same as what would have been seen in the placebo group. We infer a placebo volunteer's response to the HIV vaccine using their rabies response and a prediction model from the vaccine group. The second method entails vaccinating all uninfected placebo patients at the closeout of the trial with the HIV vaccine and recording immune response. We pretend this immune response at closeout is what they would have had at baseline. We can then infer what the distribution of immune response among placebo infecteds would have been. Such designs may help elucidate the role of immune response in preventing infections. More pointedly, they could be helpful in the decision to improve or abandon an HIV vaccine with mediocre performance in a phase III trial.

Figure 1

Made-up scatterplot illustrating imputation of the immune response to an HIV vaccine (X₀) in the placebo group based on the observed immune response to a rabies vaccine (W₀) for a single patient. The bivariate distribution between X₀, W₀ is observed in the vaccine group. Randomization assures that this distribution and regression line also apply to the placebo group. While X₀ cannot be observed in the placebo group, W₀ can and provides the basis for imputation. A very high correlation between X₀, W₀ is used to illustrate the concept.

Figure 2

Schematic representation of augmented designs. Circles and lowercase letters denote inoculations, immuneresponse is denoted by capital letters. Under a traditional design, patients are vaccinated either with HIV vaccine (h) or placebo (p) and immune response to the HIV vaccine (X = X₀) is measured shortly thereafter in the vaccine group. Under BIV, both groups are vaccinated against rabies (r) and the immune response to rabies vaccine (W = W₀) is measured prior to randomization. Under CPV, placebo patients who are uninfected at the end of the trial receive HIV vaccine at close-out and their immune response is measured then (X = X_C).

Figure 3

Sample variance of estimates of β divided by the sample variance when the X₀(1) is used. Estimates denoted by B, C, 2, and X correspond to designs using BIV alone, CPV alone, BIV + CPV, and the impossible benchmark where X₀(1) is known in everyone, respectively. For BIV alone when ρ = 0.25 the relative sample variance is enormous and off the chart for the Association scenario. One can extrapolate the behavior of the designs using CPV alone and the benchmark ρ= 0 as their behavior is free of ρ. Each symbol is based on 10,000 simulated trials.