quantileControlTest {controlTest}

Find standard error for survival quantile

Description

Find standard error for survival quantile

Usage

quantileControlTest(timevar1, censor1, timevar2, censor2, q = 0.5, B = 1000,
  seed = 1234, plots = FALSE)

Arguments

timevar1

Vector of observed survival times for sample 1 (control).

censor1

Vector of censoring indicators for sample 1 (1 = uncensored, 0 = censored).

timevar2

Vector of observed survival times for sample 2 (treatment).

censor2

Vector of censoring indicators for sample 2 (1 = uncensored, 0 = censored).

q

Quantile of interest (Default is median).

B

Number of bootstrap samples.

seed

Seed number (for reproducibility).

plots

Logical. TRUE to show Kaplan-Meier plot

Details

It is important to note the possiblilty that the estimated quantile may not be estimable in our bootstrap samples. In such cases the largest observed survival time will be considered as an estimate for the quantile.

Value

Returns quantile estimate, bootstrapped standard error, test statistic, and two-sided p-value.

References

Li, G., Tiwari, R.C., and Wells, M. (1996). "Quantile Comparison Functions in Two-Sample Problems: With Applications to Comparisons of Diagnostic Markers." Journal of the American Statistical Association, 91, 689-698.

Chakraborti, S., and Mukerjee, R. (1989), "A Confidence Interval for a Measure Associated With the Comparison of a Treatment With a Control," South African Statistical Journal, 23, 219-230.

Gastwirth, J. L., and Wang, J. L. (1988), "Control Percentile Test for Censored Data," Journal of Statistical Planning and Inference, 18, 267-276.

Examples

#Reference: Survival Analysis Techniques for Censored and Truncated Data.
#Klein and Moeschberger (1997) Springer.
#Data: Chapter 7.6 Example 7.9 (p. 211)
library(controlTest)
t1 <- c(1, 63, 105, 129, 182, 216, 250, 262, 301, 301,
       342, 354, 356, 358, 380, 383, 383, 338, 394, 408, 460, 489,
       499, 523, 524, 535, 562, 569, 675, 676, 748, 778, 786, 797,
       955, 968, 1000, 1245, 1271, 1420, 1551, 1694, 2363, 2754, 2950)
t2 <- c(17, 42, 44, 48, 60, 72, 74, 95, 103, 108, 122, 144, 167, 170,
       183, 185, 193, 195, 197, 208, 234, 235, 254, 307, 315, 401, 445,
       464, 484, 528, 542, 547, 577, 580, 795, 855, 1366, 1577, 2060,
       2412, 2486, 2796, 2802, 2934, 2988)
c1 <- c(rep(1, 43), 0, 0)
c2 <- c(rep(1, 39), rep(0, 6))
quantileControlTest(t1, c1, t2, c2, q = 0.5, B = 500)