kSampleIcens function

Non-parametric comparison of k survival curves

Weighted log-rank tests for non-parametric comparison of $k$

survival curves observed as interval-censored data. It implements an interval-censored analog to well known $G^[rho,gamma]$ class of right-censored $k$-sample tests of Fleming and Harrington (1991, Chapter 7) proposed by Gómez and Oller (2008) and described also in Gómez et al. (2009, Sec. 3).

This R implementation considerably exploited the example code shown in Gómez et al. (2009, Sec. 3.3).

kSampleIcens(A, group, icsurv, rho=0, gamma=0)

Arguments

• A: two column matrix or data.frame with lower and upper limits of observed intervals in a pooled sample. It is passed to function PGM

  from the Icens package which calculates the NPMLE of the cdf function based on a pooled sample.

• group: a vector of group indicators. Its length must be the same as number of rows in A or as number of columns in icsurv$clmat.

• icsurv: estimated cdf of based on a pooled sample. It must be an object of class icsurv obtained by using the function PGM with A matrix.

  It does not have to be supplied. Nevertheless, if supplied by the user, it is not re-calculated inside the function call which spares some computational time, especially if the test is to be run with different $rho$ and $gamma$ values.

• rho: parameter of the weighted log-rank (denoted as $rho$ in Bogaerts, Komárek and Lesaffre (2017)).

• gamma: parameter of the weighted log-rank (denoted as $gamma$ in Bogaerts, Komárek and Lesaffre (2017))

Returns

An object of class htest.

References

Fleming, T. R. and Harrington, D. P. (1991). Counting Processes and Survival Analysis. New York: Wiley.

Gómez, G. and Oller Pique, R. (2008). A new class of rank tests for interval-censored data.

Harvard University Biostatistics Working Paper Series, Working Paper 93. https://biostats.bepress.com/harvardbiostat/paper93/

Gómez, G., Calle, M. L., Oller, R., Langohr, K. (2009). Tutorial on methods for interval-censored data and their implementation in R. Statistical Modelling, 9 , 259-297.

Bogaerts, K., Komárek, A. and Lesaffre, E. (2017). Survival Analysis with Interval-Censored Data: A Practical Approach. Boca Raton: Chapman and Hall/CRC.

See Also

PGM, ictest.

Author(s)

Arnošt Komárek arnost.komarek@mff.cuni.cz

Examples

### Comparison of emergence distributions
##  of tooth 44 on boys and girls
data("tandmob", package="icensBKL")

  ## take only first 50 children here
  ## to decrease the CPU time
  ## of the example
tandmob50 <- tandmob[1:50,]

  ## only needed variables
Acompare <- subset(tandmob50, select=c("fGENDER", "L44", "R44"))

  ## left-censored observations:
  ##  change lower limit denoted by NA to 0
Acompare$L44[is.na(Acompare$L44)] <- 0

  ## right-censored observations:
  ##  change upper limit denoted by NA to 20
  ##  20 = infinity in this case
Acompare$R44[is.na(Acompare$R44)] <- 20

  ## inputs for kSampleIcens function
Amat <- Acompare[, c("L44", "R44")]
Group <- Acompare$fGENDER

  ## two-sample test
  ## (interval-censored version of classical Mantel's log-rank)
kSampleIcens(A=Amat, group=Group, rho=0, gamma=0)

  ## some other choices of rho and gamma,
  ## pooled CDF is supplied to kSampleIcens function
  ## to speed-up the calculation
  ## and also to set maxiter to higher value than above
  ## to ensure convergence
poolcdf <- PGM(A=Amat, maxiter=10000)

  ## IC version of classical Mantel's log-rank again
kSampleIcens(A=Amat, group=Group, icsurv=poolcdf, rho=0, gamma=0)

  ## IC version of Peto-Prentice generalization of
  ## the Wilcoxon test
kSampleIcens(A=Amat, group=Group, icsurv=poolcdf, rho=1, gamma=0)

kSampleIcens(A=Amat, group=Group, icsurv=poolcdf, rho=0, gamma=1)
kSampleIcens(A=Amat, group=Group, icsurv=poolcdf, rho=1, gamma=1)