coin (version 1.0-24)

photocar: Multiple Dosing Photococarcinogenicity Experiment

Description

Survival time, time to first tumor and total number of tumors for three groups of animals from a photococarcinogenicity study.

Usage

data("photocar")

source

Daniel F. Molefe, James J. Chen, Paul C. Howard, Barbara J. Miller, Christopher P. Sambuco, P. Donald Forbes & Ralph L. Kodell (2005). Tests for effects on tumor frequency and latency in multiple dosing photococarcinogenicity experiments. Journal of Statistical Planning and Inference 129, 39--58.

Torsten Hothorn, Kurt Hornik, Mark A. van de Wiel & Achim Zeileis (2006). A Lego system for conditional inference, The American Statistician, 60(3), 257--263.

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 1-3 in Molefe et al. (2005).

The main interest is testing the global null of no treatment effect with respect to survival time, time to first tumor and number of tumors (Molefe et al., 2005, analyse the detection time of tumors in addition, this data is not given here). In case the global null hypothesis can be rejected, the deviations from the partial hypotheses are of special interest.

Examples

layout(matrix(1:3, ncol = 3))
  plot(survfit(Surv(time, event) ~ group, data = photocar), xmax = 50, 
       lty =  1:3, main = "Survival Time")
  legend("bottomleft", lty = 1:3, levels(photocar$group), bty = "n")
  plot(survfit(Surv(dmin, tumor) ~ group, data = photocar), xmax = 50, 
       lty = 1:3, main = "Time to First Tumor")
  legend("bottomleft", lty = 1:3, levels(photocar$group), bty = "n")
  boxplot(ntumor ~ group, data = photocar, main = "Number of Tumors")

  ### global test (all three responses)
  fm <- Surv(time, event) + Surv(dmin, tumor) + ntumor ~ group
  it <- independence_test(fm, data = photocar, 
                          distribution = approximate(B = 10000))
  pvalue(it)

  ### why was the global null hypothesis rejected?
  statistic(it, "standardized")
  pvalue(it, "single-step")