breast: Survival after breast cancer surgery

In success: Survival Control Charts Estimation Software

Survival after breast cancer surgery

Description

Data about patient survival after their breast cancer surgery procedure performed at one of the 15 units participating in a cancer treatment study. The data is based on a trial performed at the European Organisation for Research and Treatment of Cancer (EORTC).

Usage

breast

Format

A data.frame with 2663 rows and 11 variables:

entrytime

Chronological time of entry of patient into study/time of surgery (numeric)

survtime

Time from entry until failure of patient (numeric)

censorid

Censoring indicator (0 - right censored, 1 - observed) (integer)

unit

Unit number at which patient received treatment (integer)

var1-7

Covariates associated with patient (factor)

Source

Based on trial data from the European Organisation for Research and Treatment of Cancer, https://www.eortc.org/

Examples

#Determine the estimated arrival rate for all units in the data
arrival_rate(breast)

#Plot Quality Control charts for unit 11 in the study
library(survival)
phmodbreast <- coxph(Surv(survtime, censorid) ~ . - entrytime - unit ,
data = breast)
glmmodbreast <- glm((survtime <= 36) & (censorid == 1) ~ . - entrytime - unit,
 data = breast, family = binomial(link = "logit"))

par(mfrow = c(1, 3))
p1 <- plot(cgr_cusum(data = subset(breast, unit == 11),  coxphmod = phmodbreast)) +
ggtitle("CGR-CUSUM")
p2 <- plot(bk_cusum(data = subset(breast, unit == 11),  coxphmod = phmodbreast,
 theta = log(2))) + ggtitle("BK-CUSUM")
p3 <- plot(bernoulli_cusum(data = subset(breast, unit == 11), followup = 36,
 glmmod = glmmodbreast, theta = log(2))) + ggtitle("Bernoulli CUSUM")
p4 <- plot(funnel_plot(data = breast, glmmod = glmmodbreast, followup = 36 )) +
ggtitle("Funnel plot")

if(require("gridExtra")){
 grid.arrange(p1, p2, p3, p4, nrow = 2)
}

success documentation built on June 22, 2024, 10:19 a.m.