,
Weijing Wang [aut] ,
Martin T. Wells [aut] | Title: | Tau Measure with Right-Censored Data |
|---|---|
| Description: | A clinically meaningful measures of treatment effects for right-censored data are provided, based on the concept of Kendall's tau, along with the corresponding inference procedures. Two plots of tau processes, with the option to account for the cure fraction or not, are available. The plots of tau processes serve as useful graphical tools for monitoring the relative performances over time. |
| Authors: | Yi-Cheng Tai [aut, cre, cph] ,
Weijing Wang [aut] ,
Martin T. Wells [aut] |
| Maintainer: | Yi-Cheng Tai <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 2.1.3 |
| Built: | 2025-01-30 04:39:25 UTC |
| Source: | https://github.com/s07308/tauprocess |
This dataset is obtained from 'pbc' in package 'survival' by excluding the non-randomized individuals. For background and details of the original dataset, please refer to the document page of 'survival'.
pbcpbc
pbcA data frame with 258 rows and 3 columns:
the survival of each subject in the trial (days)
censoring indicator (1: dead; 0: censored)
treatment arm (1: D-penicillamine; 0: placebo)
https://cran.r-project.org/package=survival
This function plot the estimated tau process with/withour cure fraction obtained from tau_proc.
It can be used to monitor the progression of treatment effect (for susceptible subgroups).
## S3 method for class 'tau_process' plot(x, ...)## S3 method for class 'tau_process' plot(x, ...)
x |
an object of class "tau_process", returned by |
... |
additional arguments passed to underlying plot method |
a list with components x and y.
fit <- tau_proc(pbc$surv.time, pbc$event, pbc$arm) plot(fit)fit <- tau_proc(pbc$surv.time, pbc$event, pbc$arm) plot(fit)
This function plot the estimated tau process obtained from tau.fit.
It can be used to monitor the progression of treatment effect.
## S3 method for class 'tauFit' plot(x, ...)## S3 method for class 'tauFit' plot(x, ...)
x |
an object of class "tauFit", returned by |
... |
additional arguments passed to underlying plot method |
a list with components x and y.
fit <- tau.fit(pbc$surv.time, pbc$event, pbc$arm) plot(fit, type = "b")fit <- tau.fit(pbc$surv.time, pbc$event, pbc$arm) plot(fit, type = "b")
user-friendly format to present the inference results obtained from summary.tauFit.
## S3 method for class 'summaryTauFit' print(x, ...)## S3 method for class 'summaryTauFit' print(x, ...)
x |
an object of class "summaryTauFit" |
... |
additional arguments passed to underlying |
None
fit <- tau.fit(pbc$surv.time, pbc$event, pbc$arm) summary(fit)fit <- tau.fit(pbc$surv.time, pbc$event, pbc$arm) summary(fit)
This function summarizes the inference results obtained from tau.fit.
The results under random grouping design (complete randomization design) and fixed grouping design (random allocation rule / urn model) would be almost the same with large sample size.
## S3 method for class 'tauFit' summary(object, conf.int = 0.95, ...)## S3 method for class 'tauFit' summary(object, conf.int = 0.95, ...)
object |
an object of class "tauFit" |
conf.int |
the significance level of the confidence interval |
... |
additional arguments passed to underlying summary method |
an object of class "summaryTauFit"
fit <- tau.fit(pbc$surv.time, pbc$event, pbc$arm) summary(fit)fit <- tau.fit(pbc$surv.time, pbc$event, pbc$arm) summary(fit)
Estimate the Tau Process with/without cure fraction
tau_proc(time, status, arm, t = NULL, cure = FALSE)tau_proc(time, status, arm, t = NULL, cure = FALSE)
time |
a numerical vector of survival times. |
status |
endpoint status (0 for censored, 1 for deceased). |
arm |
treatment group indicator (1 for treated, 0 for control). |
t |
a sequence of specified times. If the user do not specify the sequence, the default is an equally-spaced sequence from 0 to the last identified time. |
cure |
a boolean variable indicating whether to consider the cure fractions. |
The estimation method proposed by Yi-Cheng Tai, Weijing Wang and Martin T. Wells to estimate tau process with or without cure fraction.
an object of class "tau_process" with components
t |
the specified sequence of time points |
vals_tau_proc |
the estimated value of tau measure at given time |
cure |
a boolean variable indicating whether to consider the cure fraction |
cure_rates |
the estimated cure rates for Group 0 and Group 1, respectively |
tau_proc(pbc$surv.time, pbc$event, pbc$arm, cure = TRUE)tau_proc(pbc$surv.time, pbc$event, pbc$arm, cure = TRUE)
Estimate the tau process at specified time points. The estimated variances at the last time point under complete randomization design and random allocation rule (urn model) are provided.
tau.fit(time, status, arm, t = numeric())tau.fit(time, status, arm, t = numeric())
time |
a numerical vector of survival times. |
status |
endpoint status (0 for censored, 1 for deceased). |
arm |
treatment group indicator (1 for treated, 0 for control). |
t |
a sequence of specified times. If the user do not specify the sequence, the default is an equally-spaced sequence from 0 to the last identified time. |
The estimation and inference procedure are proposed by Yi-Cheng Tai, Weijing Wang and Martin T. Wells. The value of tau measure serves as a clinically meaningful measure of treatment effect. It supplements the traditional hazard ratio (HR) under nonproportional hazard scenario.
an object of class "tauFit" with components
N0 |
number of individuals with arm=0 |
N1 |
number of individuals with arm=1 |
t |
the specified truncation time |
tau |
the estimated value of tau measure |
var.r |
the estimated variance under random grouping design (complete randomization design) |
var.f |
the estimated variance under fixed grouping design (random allocation rule / urn model) |
tau.fit(pbc$surv.time, pbc$event, pbc$arm)tau.fit(pbc$surv.time, pbc$event, pbc$arm)