| Title: | Yang and Prentice Model with Piecewise Exponential Baseline Distribution |
| Version: | 1.0.1 |
| Description: | Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution. Details about the model can be found in Demarqui and Mayrink (2019) <doi:10.48550/arXiv.1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/fndemarqui/YPPE |
| BugReports: | https://github.com/fndemarqui/YPPE/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| Biarch: | true |
| Depends: | R (≥ 3.4.0), survival |
| Imports: | methods, MASS, Formula, Rcpp (≥ 0.12.0), rstan (≥ 2.18.1), rstantools (≥ 2.0.0) |
| LinkingTo: | BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0) |
| SystemRequirements: | GNU make |
| RoxygenNote: | 7.0.2 |
| Suggests: | testthat |
| NeedsCompilation: | yes |
| Packaged: | 2020-01-09 14:17:28 UTC; fndemarqui |
| Author: | Fabio Demarqui [aut, cre] |
| Maintainer: | Fabio Demarqui <fndemarqui@est.ufmg.br> |
| Repository: | CRAN |
| Date/Publication: | 2020-01-09 20:40:03 UTC |
The 'YPPE' package.
Description
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution curves. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
References
Demarqui, F. N. and Mayrink, V. D. (2019). A fully likelihood-based approach to model survival data with crossing survival curves. <arXiv:1910.02406>
Stan Development Team (2019). RStan: the R interface to Stan. R package version 2.19.2. https://mc-stan.org
Yang, S. and Prentice, R. L. (2005). Semiparametric analysis of short-term and long-term hazard ratios with two-sample survival data. Biometrika 92, 1-17.
Generic S3 method coef
Description
Generic S3 method coef
Usage
coef(object, ...)
Arguments
object |
a fitted model object |
... |
further arguments passed to or from other methods. |
Value
the estimated regression coefficients
Estimated regression coefficients
Description
Estimated regression coefficients
Usage
## S3 method for class 'yppe'
coef(object, ...)
Arguments
object |
an object of the class yppe |
... |
further arguments passed to or from other methods |
Value
the estimated regression coefficients
Generic S3 method confint
Description
Generic S3 method confint
Usage
confint(object, ...)
Arguments
object |
a fitted model object |
... |
further arguments passed to or from other methods. |
Value
the estimated regression coefficients
Confidence intervals for the regression coefficients
Description
Confidence intervals for the regression coefficients
Usage
## S3 method for class 'yppe'
confint(object, level = 0.95, ...)
Arguments
object |
an object of the class yppe |
level |
the confidence level required |
... |
further arguments passed to or from other methods |
Value
100(1-alpha) confidence intervals for the regression coefficients
Generic S3 method crossTime
Description
Generic S3 method crossTime
Usage
crossTime(object, ...)
Arguments
object |
a fitted model object |
... |
further arguments passed to or from other methods. |
Value
the crossing survival time
Computes the crossing survival times
Description
Computes the crossing survival times
Usage
## S3 method for class 'yppe'
crossTime(object, newdata1, newdata2, conf.level = 0.95, nboot = 4000, ...)
Arguments
object |
an object of class yppe |
newdata1 |
a data frame containing the first set of explanatory variables |
newdata2 |
a data frame containing the second set of explanatory variables |
conf.level |
level of the confidence/credible intervals |
nboot |
number of bootstrap samples (default nboot=4000); ignored if approach="bayes". |
... |
further arguments passed to or from other methods. |
Value
the crossing survival time
Examples
# ML approach:
library(YPPE)
mle <- yppe(Surv(time, status)~arm, data=ipass, approach="mle")
summary(mle)
newdata1 <- data.frame(arm=0)
newdata2 <- data.frame(arm=1)
tcross <- crossTime(mle, newdata1, newdata2)
tcross
ekm <- survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(mle, newdata)
time <- sort(ipass$time)
plot(ekm, col=1:2)
lines(time, St[[1]])
lines(time, St[[2]], col=2)
abline(v=tcross, col="blue")
# Bayesian approach:
bayes <- yppe(Surv(time, status)~arm, data=ipass, approach="bayes")
summary(bayes)
newdata1 <- data.frame(arm=0)
newdata2 <- data.frame(arm=1)
tcross <- crossTime(bayes, newdata1, newdata2)
tcross
ekm <- survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(bayes, newdata)
time <- sort(ipass$time)
plot(ekm, col=1:2)
lines(time, St[[1]])
lines(time, St[[2]], col=2)
abline(v=tcross, col="blue")
Gastric cancer data set
Description
Data set from a clinical trial conducted by the Gastrointestinal Tumor Study Group (GTSG) in 1982. The data set refers to the survival times of patients with locally nonresectable gastric cancer. Patients were either treated with chemotherapy combined with radiation or chemotherapy alone.
Format
A data frame with 90 rows and 3 variables:
time: survival times (in days)
status: failure indicator (1 - failure; 0 - otherwise)
trt: treatments (1 - chemotherapy + radiation; 0 - chemotherapy alone)
Author(s)
Fabio N. Demarqui fndemarqui@est.ufmg.br
References
Gastrointestinal Tumor Study Group. (1982) A Comparison of Combination Chemotherapy and Combined Modality Therapy for Locally Advanced Gastric Carcinoma. Cancer 49:1771-7.
IRESSA Pan-Asia Study (IPASS) data set
Description
Reconstructed IPASS clinical trial data reported in Argyropoulos and Unruh (2015). Although reconstructed, this data set preserves all features exhibited in references with full access to the observations from this clinical trial. The data base is related to the period of March 2006 to April 2008. The main purpose of the study is to compare the drug gefitinib against carboplatin/paclitaxel doublet chemotherapy as first line treatment, in terms of progression free survival (in months), to be applied to selected non-small-cell lung cancer (NSCLC) patients.
Format
A data frame with 1217 rows and 3 variables:
time: progression free survival (in months)
status: failure indicator (1 - failure; 0 - otherwise)
arm: (1 - gefitinib; 0 - carboplatin/paclitaxel doublet chemotherapy)
Author(s)
Fabio N. Demarqui fndemarqui@est.ufmg.br
References
Argyropoulos, C. and Unruh, M. L. (2015). Analysis of time to event outcomes in randomized controlled trials by generalized additive models. PLOS One 10, 1-33.
Print the summary.yppe output
Description
Print the summary.yppe output
Usage
## S3 method for class 'summary.yppe'
print(x, ...)
Arguments
x |
an object of the class summary.yppe. |
... |
further arguments passed to or from other methods. |
Value
a summary of the fitted model.
Summary for the yppe model
Description
Summary for the yppe model
Usage
## S3 method for class 'yppe'
summary(object, ...)
Arguments
object |
an objecto of the class 'yppe'. |
... |
further arguments passed to or from other methods. |
Generic S3 method survfit
Description
Generic S3 method survfit
Usage
survfit(object, ...)
Arguments
object |
a fitted model object |
... |
further arguments passed to or from other methods. |
Value
the crossing survival time
Survival function for the YPPE model
Description
Survival function for the YPPE model
Usage
## S3 method for class 'yppe'
survfit(object, newdata, ...)
Arguments
object |
an object of the class yppe |
newdata |
a data frame containing the set of explanatory variables. |
... |
further arguments passed to or from other methods. |
Value
a list containing the estimated survival probabilities.
Examples
# ML approach:
library(YPPE)
mle <- yppe(Surv(time, status)~arm, data=ipass, approach="mle")
summary(mle)
ekm <- survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(mle, newdata)
time <- sort(ipass$time)
plot(ekm, col=1:2)
lines(time, St[[1]])
lines(time, St[[2]], col=2)
# Bayesian approach:
bayes <- yppe(Surv(time, status)~arm, data=ipass, approach="bayes")
summary(bayes)
ekm <- survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(bayes, newdata)
time <- sort(ipass$time)
plot(ekm, col=1:2)
lines(time, St[[1]])
lines(time, St[[2]], col=2)
Time grid
Description
Time grid
Usage
timeGrid(time, status, n_int = NULL)
Arguments
time |
Vector of failure times |
status |
Vector of failure indicators |
n_int |
Optional. Number of intervals. If |
Value
Time grid.
Generic S3 method vcov
Description
Generic S3 method vcov
Usage
vcov(object, ...)
Arguments
object |
a fitted model object |
... |
further arguments passed to or from other methods. |
Value
the variance-covariance matrix associated the regression coefficients.
Covariance of the regression coefficients
Description
Covariance of the regression coefficients
Usage
## S3 method for class 'yppe'
vcov(object, ...)
Arguments
object |
an object of the class yppe |
... |
further arguments passed to or from other methods. |
Value
the variance-covariance matrix associated with the regression coefficients.
Fits the Yang and Prentice model with baseline distribution modelled by the piecewise exponential distribution.
Description
Fits the Yang and Prentice model with baseline distribution modelled by the piecewise exponential distribution.
Usage
yppe(
formula,
data,
n_int = NULL,
rho = NULL,
tau = NULL,
hessian = TRUE,
approach = c("mle", "bayes"),
hyper_parms = list(h1_gamma = 0, h2_gamma = 4, mu_psi = 0, sigma_psi = 4, mu_phi = 0,
sigma_phi = 4, mu_beta = 0, sigma_beta = 4),
...
)
Arguments
formula |
an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. |
data |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which yppe is called. |
n_int |
number of intervals of the PE distribution. If NULL, default value (square root of n) is used. |
rho |
the time grid of the PE distribution. If NULL, the function timeGrid is used to compute rho. |
tau |
the maximum time of follow-up. If NULL, tau = max(time), where time is the vector of observed survival times. |
hessian |
logical; If TRUE (default), the hessian matrix is returned when approach="mle". |
approach |
approach to be used to fit the model (mle: maximum likelihood; bayes: Bayesian approach). |
hyper_parms |
a list containing the hyper-parameters of the prior distributions (when approach = "bayes"). If not specified, default values are used. |
... |
Arguments passed to either 'rstan::optimizing' or 'rstan::sampling' . |
Value
yppe returns an object of class "yppe" containing the fitted model.
Examples
# ML approach:
library(YPPE)
mle <- yppe(Surv(time, status)~arm, data=ipass, approach="mle")
summary(mle)
# Bayesian approach:
bayes <- yppe(Surv(time, status)~arm, data=ipass, approach="bayes")
summary(bayes)