| Title: | Quantile Comparison for Two-Sample Right-Censored Survival Data |
|---|---|
| Description: | Nonparametric two-sample procedure for comparing survival quantiles. |
| Authors: | Eric S. Kawaguchi [aut, cre] |
| Maintainer: | Eric S. Kawaguchi <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.0 |
| Built: | 2024-11-10 04:59:36 UTC |
| Source: | https://github.com/erickawaguchi/controltest |
Find standard error for survival quantile
quantileControlTest(timevar1, censor1, timevar2, censor2, q = 0.5, B = 1000, seed = 1234, plots = FALSE)quantileControlTest(timevar1, censor1, timevar2, censor2, q = 0.5, B = 1000, seed = 1234, plots = FALSE)
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 (in terms of CDF). Default is median. |
B |
Number of bootstrap samples. |
seed |
Seed number (for reproducibility). |
plots |
Logical. TRUE to show plot of cumulative distribution functions. |
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.
Returns quantile estimate, bootstrapped standard error, test statistic, and two-sided p-value.
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.
#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)#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
quantileSE(timevar, censor, q = 0.5, B = 1000, alpha = 0.05, seed = 1991, plots = FALSE)quantileSE(timevar, censor, q = 0.5, B = 1000, alpha = 0.05, seed = 1991, plots = FALSE)
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 |
Returns quantile estimate, bootstrapped standard error, and (1 - alpha / 2) * 100
#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)#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)
Supremum-type test for two-sample comparison of survival quantiles.
supControlTest(timevar1, censor1, timevar2, censor2, q.min = NULL, q.max = 0.5, gridpts = 50, B = 500, seed = 1234, plots = FALSE)supControlTest(timevar1, censor1, timevar2, censor2, q.min = NULL, q.max = 0.5, gridpts = 50, B = 500, seed = 1234, plots = FALSE)
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.min |
Smallest quantile (in terms of CDF) to test. Default is the time to earliest event for sample 1. |
q.max |
Largest quantile (in terms of CDF) to test. |
gridpts |
Number of grid points between q.min and q.max to test. |
B |
Number of bootstrap samples. |
seed |
Seed number (for reproducibility). |
plots |
Logical. TRUE to show plot of cumulative distribution functions. |
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.
Returns quantile estimate, bootstrapped standard error, test statistic, and two-sided p-value.
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.
#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)) supControlTest(t1, c1, t2, c2, q.max = 0.5, B = 500)#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)) supControlTest(t1, c1, t2, c2, q.max = 0.5, B = 500)