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 and `TRUE` otherwise.

`dmin`

  time to first tumor.

`tumor`

  status indicator for `dmin`: `FALSE` when no tumor was observed and `TRUE` 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 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.

### 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")

[Package coin version 1.3-0 Index]