# Basic package workflow

    library(vaccine)
    #> vaccine (version 0.1.0).
    #> Type ?vaccine to get started.

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_env",
      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)
    #> Warning: package 'SuperLearner' was built under R version
    #> 4.2.3
    #> Loading required package: nnls
    #> Loading required package: gam
    #> Warning: package 'gam' was built under R version 4.2.3
    #> Loading required package: splines
    #> Loading required package: foreach
    #> Loaded gam 1.22-2
    #> Super Learner
    #> Version: 2.0-28
    #> Package created on 2021-05-04

The `plot_ce` function produces basic plots of CR or CVE curves. These are returned as ggplot2 objects, so they can be further customized as needed.

    plot_ce(ests_cox, ests_np)

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