---
title: "Introduction to `savvyr`"
package: savvyr
bibliography: "../inst/REFERENCES.bib"
output:
  rmarkdown::html_vignette:
          toc: true
vignette: |
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{Introduction to `savvyr`}
  %\VignetteEngine{knitr::rmarkdown}
editor_options:
  chunk_output_type: console
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(savvyr)
library(kableExtra)
```

In this vignette we give an introduction how to use `savvyr` to estimate adverse event probabilities using the SAVVY framework.

# Example using dummy data

We generate the dataset $S1$ in @stegherr_meta_analytic_2021 using the parameter
values for Arm A.
First we define the sample size and a range of censoring times.
Then we set the hazard
of the three event types (adverse event, death/hard competing event and soft
competing event). After the dataset has been generated, we set $\tau$ as the
maximum event time.

```{r, include=TRUE, echo=TRUE}
n <- 200

min_cens <- 0
max_cens <- 1000

set.seed(2020)
dat1 <- generate_data(
  n,
  cens = c(min_cens, max_cens),
  haz_ae = 0.00265,
  haz_death = 0.00151,
  haz_soft = 0.00227
)

tau <- max(dat1[, "time_to_event"])
```

The structure of the dataset looks as follows:

```{r, include=TRUE, echo=TRUE}
kable(head(dat1, 10), align = c("crcr"))
```

For this dataset we then compute all the estimators used in the comparisons
in @stegherr_survival_2021 and @stegherr_estimating_2021.
We start with the estimators that do not account for competing events (incidence
proportion, incidence density, Inverse Kaplan Meier), then incidence proportion
accounting for competing events and Aalen-Johansen (both first with death only
as hard competing event, then using all competing events):

```{r, include=TRUE, echo=TRUE}
ip <- inc_prop(dat1, tau)
id <- prop_trans_inc_dens(dat1, tau)
km <- one_minus_kaplan_meier(dat1, tau)

idce_2 <- prop_trans_inc_dens_ce(dat1, ce = 2, tau)
aj_2 <- aalen_johansen(dat1, ce = 2, tau)

idce_3 <- prop_trans_inc_dens_ce(dat1, ce = 3, tau)
aj_3 <- aalen_johansen(dat1, ce = 3, tau)
```

The AE risks look as follows:

```{r, include=TRUE, echo=TRUE}
tab <- rbind(ip, id, km, idce_2, aj_2[1:2], idce_3, aj_3[1:2])
colnames(tab) <- c(
  "estimated AE probability",
  "variance of estimation"
)
rownames(tab) <- c(
  "incidence proportion",
  "probability transform incidence density ignoring competing event",
  "1 - Kaplan-Meier", "probability transform incidence density (death only)",
  "Aalen-Johansen (death only), AE risk", "probability transform incidence density (all CEs)",
  "Aalen-Johansen (all CEs), AE risk"
)

kable(tab, digits = c(3, 5))
```

Finally, the estimated probabilities of competing events based on the
Aalen-Johansen estimators:

```{r, include=TRUE, echo=TRUE}
tab <- rbind(aj_2[3:4], aj_3[3:4])
colnames(tab) <- c(
  "estimated probability",
  "variance of estimation"
)
rownames(tab) <- c(
  "Aalen-Johansen (death only), CE risk",
  "Aalen-Johansen (all CEs), CE risk"
)

kable(tab, digits = c(3, 5))
```

# References
