| Title: | Yang and Prentice Model with Baseline Distribution Modeled by Bernstein Polynomials |
| Version: | 0.0.1 |
| Description: | Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) <doi:10.48550/arXiv.1910.04475>. Model fitting can be carried out via both maximum likelihood 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/YPBP |
| BugReports: | https://github.com/fndemarqui/YPBP/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| Biarch: | true |
| Depends: | R (≥ 3.4.0), survival |
| Imports: | Formula, MASS, methods, 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: | 7.1.0 |
| Suggests: | knitr, testthat |
| NeedsCompilation: | yes |
| Packaged: | 2020-06-26 15:33:48 UTC; fndemarqui |
| Author: | Fabio Demarqui 
[aut, cre] |
| Maintainer: | Fabio Demarqui <fndemarqui@est.ufmg.br> |
| Repository: | CRAN |
| Date/Publication: | 2020-06-29 09:20:13 UTC |
The 'YPBP' package.
Description
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.04475>. Model fitting can be carried out via maximum likelihood or Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
References
Demarqui, F. N. and Mayrink, V. D. (2019). An Unified Semiparametric Approach to Model Lifetime Data with Crossing Survival Curves. <arXiv:1910.04475>
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.
Stan Development Team (2019). RStan: the R interface to Stan. R package version 2.19.2. https://mc-stan.org
Estimated regression coefficients
Description
This function returns the estimated regression coefficients when the maximum likelihood estimation approach is used in the model fitting.
Usage
## S3 method for class 'ypbp'
coef(object, ...)
Arguments
object |
an object of the class ypbp. |
... |
further arguments passed to or from other methods. |
Value
the estimated regression coefficients.
Examples
fit <- ypbp(Surv(time, status)~arm, data=ipass)
coef(fit)
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 confidence intervals for the regression coefficients
Confidence intervals for the regression coefficients
Description
This function returns the estimated confidence intervals for the regression coefficients when the maximum likelihood estimation approach is used in the model fitting.
Usage
## S3 method for class 'ypbp'
confint(object, level = 0.95, ...)
Arguments
object |
an object of the class ypbp. |
level |
the confidence level required. |
... |
further arguments passed to or from other methods. |
Value
A matrix (or vector) with columns giving lower and upper confidence limits for the regression coefficients. These will be labeled as (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%).
Examples
fit <- ypbp(Surv(time, status)~arm, data=ipass)
confint(fit)
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 along with their corresponding confidence/credible intervals.
Usage
## S3 method for class 'ypbp'
crossTime(object, newdata1, newdata2, conf.level = 0.95, nboot = 4000, ...)
Arguments
object |
an object of class ypbp |
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; default is conf.level = 0.95 |
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(YPBP)
mle <- ypbp(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, nboot = 100)
tcross
ekm <- survival::survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(mle, newdata)
plot(ekm, col=1:2)
with(St, lines(time, surv[[1]]))
with(St, lines(time, surv[[2]], col=2))
abline(v=tcross, col="blue")
# Bayesian approach:
bayes<-ypbp(Surv(time,status)~arm,data=ipass,approach="bayes",chains=2,iter=100)
summary(bayes)
newdata1 <- data.frame(arm=0)
newdata2 <- data.frame(arm=1)
tcross <- crossTime(bayes, newdata1, newdata2)
tcross
ekm <- survival::survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(bayes, newdata)
plot(ekm, col=1:2)
with(St, lines(time, surv[[1]]))
with(St, lines(time, surv[[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)
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)
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.
Model.matrix method for ypbp models
Description
Reconstruct the model matrix (or matrices if the alternative formulation of the YP model is used) for a ypbp model.
Usage
## S3 method for class 'ypbp'
model.matrix(object, ...)
Arguments
object |
an object of the class ypbp. |
... |
further arguments passed to or from other methods. |
Value
The model matrix (or matrices) for the fit.
Examples
fit <- ypbp(Surv(time, status)~arm, data=ipass)
model.matrix(fit)
Print the summary.ypbp output
Description
Print the summary.ypbp output
Usage
## S3 method for class 'summary.ypbp'
print(x, ...)
Arguments
x |
an object of the class summary.ypbp. |
... |
further arguments passed to or from other methods. |
Value
a summary of the fitted model.
Summary for the ypbp model
Description
Summary for the ypbp model
Usage
## S3 method for class 'ypbp'
summary(object, ...)
Arguments
object |
an objecto of the class 'ypbp'. |
... |
further arguments passed to or from other methods. |
survfit method for ypbp models
Description
Computes the predicted survivor function for a ypbp model.
Usage
## S3 method for class 'ypbp'
survfit(formula, newdata, ...)
Arguments
formula |
an object of the class ypbp |
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(YPBP)
mle <- ypbp(Surv(time, status)~arm, data=ipass, approach="mle")
summary(mle)
ekm <- survival::survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(mle, newdata)
plot(ekm, col=1:2)
with(St, lines(time, surv[[1]]))
with(St, lines(time, surv[[2]], col=2))
# Bayesian approach:
bayes <- ypbp(Surv(time, status) ~ arm, data = ipass,
approach = "bayes", chains = 2, iter = 100)
summary(bayes)
ekm <- survival::survfit(Surv(time, status)~arm, data=ipass)
newdata <- data.frame(arm=0:1)
St <- survfit(bayes, newdata)
plot(ekm, col=1:2)
with(St, lines(time, surv[[1]]))
with(St, lines(time, surv[[2]], col=2))
Variance-covariance matrix for a ypbp model
Description
This function extracts and returns the variance-covariance matrix associated with the regression coefficients when the maximum likelihood estimation approach is used in the model fitting.
Usage
## S3 method for class 'ypbp'
vcov(object, ...)
Arguments
object |
an object of the class ypbp. |
... |
further arguments passed to or from other methods. |
Value
the variance-covariance matrix associated with the regression coefficients.
Fits the Yang and Prentice using Bernstein polynomials to model the baseline distribution.
Description
Fits the Yang and Prentice model with either the baseline hazard hazard or the baseline odds modeled via Bernstein polynomials.
Usage
ypbp(
formula,
data,
degree = NULL,
tau = NULL,
approach = c("mle", "bayes"),
baseline = c("hazard", "odds"),
hessian = TRUE,
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 ypbp is called. |
degree |
number of intervals of the PE distribution. If NULL, default value (square root of n) is used. |
tau |
the maximum time of follow-up. If NULL, tau = max(time), where time is the vector of observed survival times. |
approach |
approach to be used to fit the model (mle: maximum likelihood; bayes: Bayesian approach). |
baseline |
baseline function to be modeled. |
hessian |
logical; If TRUE (default), the hessian matrix is returned when approach="mle". |
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
ypbp returns an object of class "ypbp" containing the fitted model.
Examples
library(YPBP)
mle1 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "hazard")
mle2 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "odds")
bayes1 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "hazard",
approach = "bayes", chains = 2, iter = 500)
bayes2 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "odds",
approach = "bayes", chains = 2, iter = 500)