| Type: | Package |
| Title: | Quantile Comparison for Two-Sample Right-Censored Survival Data |
| Version: | 1.1.0 |
| Author: | Eric S. Kawaguchi [aut, cre] |
| Maintainer: | Eric S. Kawaguchi <erickawaguchi@ucla.edu> |
| Description: | Nonparametric two-sample procedure for comparing survival quantiles. |
| Encoding: | UTF-8 |
| Imports: | survival (≥ 2.41), graphics (≥ 3.4.0), stats (≥ 3.4.0) |
| LazyData: | true |
| License: | GPL-3 |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-11-21 16:50:33 UTC; erickawaguchi |
| Repository: | CRAN |
| Date/Publication: | 2017-12-07 23:58:50 UTC |
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)
Find standard error for survival quantile
Description
Find standard error for survival quantile
Usage
quantileSE(timevar, censor, q = 0.5, B = 1000, alpha = 0.05,
seed = 1991, plots = FALSE)
Arguments
timevar |
Vector of observed survival times. |
censor |
Vector of censoring indicators (1 = uncensored, 0 = censored). |
q |
Quantile of interest (Default is median). |
B |
Number of bootstrap samples. |
alpha |
Significance level for confidence interval of quantile. |
seed |
Seed number (for reproducibility). |
plots |
Logical. TRUE to show Kaplan-Meier plot |
Value
Returns quantile estimate, bootstrapped standard error, and (1 - alpha / 2) * 100
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)
c1 <- c(rep(1, 43), 0, 0)
quantileSE(timevar = t1, censor = c1, q = 0.5, B = 500)