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Basic package workflow

library(vaccine)
#> vaccine (version 1.2.1).
#> Type ?vaccine to get started.
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)
#> Number of subjects (vaccine group, phase-1): 1161
#> Number of subjects (placebo group, phase-1): 1141
#> Number of subjects (vaccine group, phase-2): 150
#> Number of subjects (placebo group, phase-2): 39
#> Number of events (vaccine group, phase-1): 27
#> Number of events (placebo group, phase-1): 21
#> Number of events (vaccine group, phase-2): 25
#> Number of events (placebo group, phase-2): 19
#> Proportion of subjects with an event (vaccine group, phase- 1): 0.02326
#> Proportion of subjects with an event (placebo group, phase- 1): 0.0184
#> Proportion of subjects with an event (vaccine group, phase- 2): 0.16667
#> Proportion of subjects with an event (placebo group, phase- 2): 0.48718

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")
#>   stat   group         est          se    ci_lower   ci_upper
#> 1 risk vaccine  0.04067009 0.008230842  0.02506853 0.05602199
#> 2 risk placebo  0.02879861 0.006563785  0.01622360 0.04121288
#> 3   ve    both -0.41222411 0.430451788 -1.56659984 0.22294979
est_overall(dat=dat, t_0=578, method="Cox")
#>   stat   group         est          se    ci_lower   ci_upper
#> 1 risk vaccine  0.04177642 0.008111679  0.02847302 0.06090588
#> 2 risk placebo  0.02938706 0.006486545  0.01901930 0.04514638
#> 3   ve    both -0.42159246 0.417915188 -1.52937491 0.20101796

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)
#> Loading required package: nnls
#> Loading required package: gam
#> Loading required package: splines
#> Loading required package: foreach
#> Loaded gam 1.22-3
#> Super Learner
#> Version: 2.0-28.1
#> Package created on 2021-05-04
#> Loading required namespace: ranger

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)
#>            x         y   ci_lower  ci_upper    curve
#> 1 0.02356062 0.1531213 0.07439247 0.2891413 ests_cox
#> 2 0.04712124 0.1513980 0.07449740 0.2833745 ests_cox
#> 3 0.07068186 0.1496917 0.07459291 0.2777089 ests_cox
#> 4 0.09424248 0.1480023 0.07467853 0.2721456 ests_cox
#> 5 0.11780310 0.1463297 0.07475377 0.2666853 ests_cox
#> 6 0.14136372 0.1446737 0.07481808 0.2613289 ests_cox

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.