Basic package workflow"

In vaccine: Statistical Tools for Immune Correlates Analysis of Vaccine Clinical Trial Data

knitr::opts_chunk$set(collapse=TRUE, comment = "#>", fig.width=6, fig.height=4, fig.align = "center")

library(vaccine)
set.seed(123)

The load_data function takes in raw data and creates a data object that can be accepted by various estimation functions. We use publicly-avaliable data from the HVTN 505 HIV vaccine efficacy trial as our example.

data(hvtn505)

dat <- load_data(
  time = "HIVwk28preunblfu",
  event = "HIVwk28preunbl",
  vacc = "trt",
  marker = "IgG_V2",
  covariates = c("age","BMI","bhvrisk"),
  weights = "wt",
  ph2 = "casecontrol",
  data = hvtn505
)

The summary_stats function gives us some useful summaries of the dataset.

summary_stats(dat)

The est_overall function allows us to estimate overall risk in the placebo and vaccine groups, as well as estimate vaccine efficacy, using either a nonparametric Kaplan-Meier estimator or a marginalized Cox model.

est_overall(dat=dat, t_0=578, method="KM")
est_overall(dat=dat, t_0=578, method="Cox")

The est_ce function allows us to compute controlled effects curves; see Gilbert, Fong, Kenny, and Carone 2022 for more detail.

ests_cox <- est_ce(dat=dat, type="Cox", t_0=578)
ests_np <- est_ce(dat=dat, type="NP", t_0=578)

The plot_ce function produces basic plots of CR or CVE curves.

plot_ce(ests_cox, ests_np)

Use the density option to add a kernel density estimate of the distribution of the marker to the plot background.

plot_ce(ests_cox, ests_np, density_type="kde", dat=dat)

Use the trim function to truncate the display of the curves, based on quantiles of the marker distribution. It is recommended to truncate the display of the nonparametric curves, as estimates can be biased towards the endpoints of the marker distribution.

ests_cox <- trim(ests_cox, dat=dat, quantiles=c(0.05,0.95))
ests_np <- trim(ests_np, dat=dat, quantiles=c(0.1,0.9))
plot_ce(ests_cox, ests_np, density_type="kde", dat=dat)

Plots generated using plot_ce can be further customized using ggplot2 functions. For example, we change the plot labels and colors as follows.

library(ggplot2)
my_plot <- plot_ce(ests_cox, ests_np, density_type="kde", dat=dat)

my_plot +
  labs(x="IgG Binding to V1V2") +
  scale_color_manual(labels = c("Cox model", "Nonparametric"),
                     values = c("darkorchid3", "deepskyblue3")) +
  scale_fill_manual(labels = c("Cox model", "Nonparametric"),
                    values = c("darkorchid3", "deepskyblue3"))

To view estimates in tabular format, use the as_table function.

ests_table <- as_table(ests_cox, ests_np)
head(ests_table)

vaccine documentation built on April 4, 2025, 12:12 a.m.