# success

# SUrvival Control
Chart EStimation Software

The goal of the package is to allow easy applications of continuous
time CUSUM procedures on survival data. Specifically, the Biswas &
Kalbfleisch CUSUM (2008) and the CGR-CUSUM (Gomon et al. 2022).

Besides continuous time procedures, it is also possible to construct
the Bernoulli (binary) CUSUM and funnel plot (Spiegelhalter 2005) on
survival data.

## Installation

You can install the released version of success from CRAN with:

`install.packages("success")`

And the development version from GitHub with:

```
# install.packages("devtools")
devtools::install_github("d-gomon/success")
```

## CGR-CUSUM Example

This is a basic example which shows you how to construct a CGR-CUSUM
chart on a hospital from the attached data set “surgerydat”:

```
dat <- subset(surgerydat, unit == 1)
exprfit <- as.formula("Surv(survtime, censorid) ~ age + sex + BMI")
tcoxmod <- coxph(exprfit, data = surgerydat)
cgr <- cgr_cusum(data = dat, coxphmod = tcoxmod, stoptime = 200)
plot(cgr)
```

You can plot the figure with control limit `h = 10`

by
using:

And determine the runlength of the chart when using control limit
`h = 10`

:

```
runlength(cgr, h = 10)
#> [1] 151
```

Using a control limit of `h = 10`

Hospital 1 would be
detected by a CGR-CUSUM 151 days after the first patient entered the
study.

## References

Gomon D., Putter H., Nelissen R.G.H.H., van der Pas S (2022): CGR-CUSUM: A
Continuous time Generalized Rapid Response Cumulative Sum chart,
*Biostatistics*

Biswas P. and Kalbfleisch J.D. (2008): A risk-adjusted CUSUM in
continuous time based on the Cox model, *Statistics in
Medicine*

Spiegelhalter D.J. (2005): Funnel plots for comparing
institutional performance, *Statistics in Medicine*