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:

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:

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 NULL, the number of intervals is set to be equal to the number of distinct observed failure times.

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)