photocar
Multiple Dosing Photococarcinogenicity Experiment
Survival time, time to first tumor, and total number of tumors in three groups of animals in a photococarcinogenicity study.
- Keywords
- datasets
Usage
photocar
Details
The animals were exposed to different levels of ultraviolet radiation (UVR) exposure (group A: topical vehicle and 600 Robertson--Berger units of UVR, group B: no topical vehicle and 600 Robertson--Berger units of UVR and group C: no topical vehicle and 1200 Robertson--Berger units of UVR). The data are taken from Tables 1 to 3 in Molefe et al. (2005).
The main interest is testing the global null hypothesis of no treatment effect with respect to survival time, time to first tumor and number of tumors. (Molefe et al., 2005, also analysed the detection time of tumors, but that data is not given here.) In case the global null hypothesis can be rejected, the deviations from the partial null hypotheses are of special interest.
Format
A data frame with 108 observations on 6 variables.
group
a factor with levels
"A"
,"B"
, and"C"
.ntumor
total number of tumors.
time
survival time.
event
status indicator for
time
:FALSE
for right-censored observations andTRUE
otherwise.dmin
time to first tumor.
tumor
status indicator for
dmin
:FALSE
when no tumor was observed andTRUE
otherwise.
References
Hothorn, T., Hornik, K., van de Wiel, M. A. and Zeileis, A. (2006). A Lego system for conditional inference. The American Statistician 60(3), 257--263. 10.1198/000313006X118430
Examples
# NOT RUN {
## Plotting data
op <- par(no.readonly = TRUE) # save current settings
layout(matrix(1:3, ncol = 3))
with(photocar, {
plot(survfit(Surv(time, event) ~ group),
lty = 1:3, xmax = 50, main = "Survival Time")
legend("bottomleft", lty = 1:3, levels(group), bty = "n")
plot(survfit(Surv(dmin, tumor) ~ group),
lty = 1:3, xmax = 50, main = "Time to First Tumor")
legend("bottomleft", lty = 1:3, levels(group), bty = "n")
boxplot(ntumor ~ group, main = "Number of Tumors")
})
par(op) # reset
## Approximative multivariate (all three responses) test
it <- independence_test(Surv(time, event) + Surv(dmin, tumor) + ntumor ~ group,
data = photocar,
distribution = approximate(nresample = 10000))
## Global p-value
pvalue(it)
## Why was the global null hypothesis rejected?
statistic(it, type = "standardized")
pvalue(it, method = "single-step")
# }