aucVardiTest: Two-Sample Tests for Growth Curves under Dependent Right...

In clinfun: Clinical Trial Design and Data Analysis Functions

Two-Sample Tests for Growth Curves under Dependent Right Censoring

Description

Permutation test for comparing growth curves across tow groups under dependent right censoring.

Usage

  aucVardiTest(meas, grp, tim=NULL, cgrps=NULL, nperm=5000)

Arguments

meas	Matrix of measurements where the rows are the subjects and columns the timepoints. At least one value should not be missing in each row. For example they can be tumor sizes measured over time.
grp	Group indicator for each subject. There must be at least two different groups. This can represent each subject's treatment.
tim	Times at which the measurements in meas are taken. If missing, the times are set to 1 through ncol(meas).
cgrps	The two groups that are being compared. If missing the first two groups will be compared.
nperm	Number of permutations for the reference distribution.

Details

The test statistic is defined as the sum of pairwise differences in the partial areas under the growth curve. For each pair of subjects the partial area is computed until the smaller of the maximum followup times. For each subject, linear interpolation is is used to fill-in missing values prior to the maximum followup time. The reference distribution of obtained by permuting the group labels.

Value

returns a list with objects ostat, pstat and p.value which are the observed test statistic for the two groups being compared, values of the statistics when the group labels are permuted

References

Vardi Y., Ying Z. and Zhang C.H. (2001). Two-Sample Tests for Growth Curves under Dependent Right Censoring. Biometrika 88, 949-960.

Examples

  grp <- sample(1:3, 100, replace=TRUE)
  grp0 <- LETTERS[grp]
  maxfup <- sample(5:20, 100, replace=TRUE)
  meas <- matrix(NA, 100, 20)
  for(i in 1:100) {
    meas[i, 1:maxfup[i]] <- cumsum((3+0.04*grp[i]) + rnorm(maxfup[i]))
  }
  aucVardiTest(meas, grp)
  aucVardiTest(meas, grp0, cgrps=c("C","B"))

clinfun documentation built on Oct. 20, 2023, 1:07 a.m.