photocar {coin} | R Documentation |
Multiple Dosing Photococarcinogenicity Experiment
Description
Survival time, time to first tumor, and total number of tumors in three groups of animals in a photococarcinogenicity study.
Usage
photocar
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.
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 analyzed 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.
Source
Molefe, D. F., Chen, J. J., Howard, P. C., Miller, B. J., Sambuco, C. P., Forbes, P. D. and Kodell, R. L. (2005). Tests for effects on tumor frequency and latency in multiple dosing photococarcinogenicity experiments. Journal of Statistical Planning and Inference 129(1–2), 39–58. doi:10.1016/j.jspi.2004.06.038
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. doi:10.1198/000313006X118430
Examples
## 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")