R/summary.PHcure.R
In a-beretta/penPHcure: Variable Selection in PH Cure Model with Time-Varying Covariates
Defines functions
summary.PHcure
Documented in
summary.PHcure
# ------------------------------------------------------------------------------
# Copyright (C) 2019 University of Liège
# <penPHcure is an R package for for estimation, variable selection and
# simulation of the semiparametric proportional-hazards (PH) cure model with
# time-varying covariates.>
# Authors: Alessandro Beretta & Cédric Heuchenne
# Contact: a.beretta@uliege.be
#
# Licence as published by the Free Software Foundation, either version 3 of the
# Licence, or any later version. This program is distributed in the hope that it
# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public Licence for more details. You should have received a copy of the GNU
# General Public Licence along with this program.
# If not, see <http://www.gnu.org/licenses/>.
# ------------------------------------------------------------------------------
#
#' @title Summary method for PHcure.object
#' @description Produces a summary of a fitted PH cure model
#' @param object an object of class \code{\link{PHcure.object}}.
#' @param conf.int a character string indicating the method to compute bootstrapped confidence intervals: \code{"percentile"} or \code{"basic"}. By default \code{conf.int = "basic"}.
#' @param conf.int.level confidence level. By default \code{conf.int.level = 0.95}.
#' @param ... ellipsis to pass extra arguments.
#' @usage
#' \method{summary}{PHcure}(object, conf.int = c("basic","percentile"), conf.int.level = 0.95,...)
#' @return An object of class \code{summary.PHcure}, a list including the following elements:
#' \item{\code{N}}{the sample size (number of individuals).}
#' \item{\code{censoring}}{the proportion of censored individuals.}
#' \item{\code{K}}{the number of unique failure times.}
#' \item{\code{isTies}}{a logical value, equal to \code{TRUE} in case of tied event times.}
#' \item{\code{conf.int}}{a character string indicating the method used to compute the bootstrapped confidence intervals: \code{"percentile"}, \code{"basic"} or \code{"no"}. The latter is returned when the \code{\link{penPHcure}} function was called with the argument \code{inference = FALSE}.}
#' \item{\code{conf.int.level}}{confidence level used to compute the bootstrapped confidence intervals.}
#' \item{\code{nboot}}{the number of bootstrap resamples for the construction of the confidence intervals.}
#' \item{\code{logL}}{the value of the log-likelihood for the estimated model.}
#' \item{\code{CURE}}{a matrix with one column containing the estimated regression coefficients in the incidence (cure) component. In case the function \code{\link{penPHcure}} was called with the argument \code{inference = TRUE}, two additional columns for the confidence intervals are provided.}
#' \item{\code{SURV}}{a matrix where in the first column the estimated regression coefficients in the latency (survival) component. In case the function \code{\link{penPHcure}} was called with the argument \code{inference = TRUE}, two additional columns for the confidence intervals are provided.}
#'
#' @examples
#' # For reproducibility
#' set.seed(12)
#' # If you use R v3.6 or greater, uncomment the following line
#' # RNGkind(sample.kind="Rounding")
#'
#' # Generate some data (for more details type ?penPHcure.simulate in your console)
#' data <- penPHcure.simulate(N=250)
#'
#' # Fit standard cure model (without inference)
#' fit <- penPHcure(Surv(time = tstart,time2 = tstop,
#' event = status) ~ z.1 + z.2 + z.3 + z.4,
#' cureform = ~ x.1 + x.2 + x.3 + x.4,data = data)
#'
#' # Use the summary method to see the results
#' summary(fit)
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficients +++
#' # ------------------------------------------------------
#' # Estimate
#' # (Intercept) 0.889668
#' # x.1 -0.972653
#' # x.2 -0.051580
#' # x.3 0.714611
#' # x.4 0.156169
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficients +++
#' # ------------------------------------------------------
#' # Estimate
#' # z.1 0.996444
#' # z.2 -0.048792
#' # z.3 -1.013562
#' # z.4 0.079422
#'
#' # Fit standard cure model (with inference). nboot = 30 bootstrap resamples
#' # are used to compute the confidence intervals.
#' fit2 <- penPHcure(Surv(time = tstart,time2 = tstop,
#' event = status) ~ z.1 + z.2 + z.3 + z.4,
#' cureform = ~ x.1 + x.2 + x.3 + x.4,data = data,
#' inference = TRUE,print.details = FALSE,nboot = 30)
#' # Use the summary method to see the results
#' summary(fit2)
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # (Intercept) 0.889668 0.455975 1.092495
#' # x.1 -0.972653 -1.414194 -0.503824
#' # x.2 -0.051580 -0.557843 0.304632
#' # x.3 0.714611 0.206211 1.081819
#' # x.4 0.156169 -0.011555 0.464841
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # z.1 0.996444 0.750321 1.130650
#' # z.2 -0.048792 -0.204435 0.073196
#' # z.3 -1.013562 -1.127882 -0.780339
#' # z.4 0.079422 -0.100677 0.193522
#' #
#' # ------------------------------------------------------
#' # * Confidence intervals computed by the basic
#' # bootstrap method, with 30 replications.
#' # ------------------------------------------------------
#'
#' # By default, confidence intervals are computed by the basic bootstrap method.
#' # Otherwise, the user can specify the percentile bootstrap method.
#' summary(fit2,conf.int = "percentile")
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # (Intercept) 0.889668 0.686842 1.323362
#' # x.1 -0.972653 -1.441483 -0.531112
#' # x.2 -0.051580 -0.407791 0.454684
#' # x.3 0.714611 0.347404 1.223011
#' # x.4 0.156169 -0.152503 0.323893
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # z.1 0.996444 0.862238 1.242567
#' # z.2 -0.048792 -0.170779 0.106852
#' # z.3 -1.013562 -1.246785 -0.899242
#' # z.4 0.079422 -0.034678 0.259521
#' #
#' # ------------------------------------------------------
#' # * Confidence intervals computed by the percentile
#' # bootstrap method, with 30 replications.
#' # ------------------------------------------------------
#'
#' # By default, a 95% confidence level is used. Otherwise, the user can specify
#' # another confidence level: e.g. 90%.
#' summary(fit2,conf.int.level = 0.90)
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficient estimates +++
#' # +++ and 90% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 5% 95%
#' # (Intercept) 0.889668 0.467864 1.074423
#' # x.1 -0.972653 -1.397088 -0.618265
#' # x.2 -0.051580 -0.527389 0.249460
#' # x.3 0.714611 0.314140 1.028425
#' # x.4 0.156169 0.033802 0.436361
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficient estimates +++
#' # +++ and 90% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 5% 95%
#' # z.1 0.996444 0.767937 1.125745
#' # z.2 -0.048792 -0.158821 0.050965
#' # z.3 -1.013562 -1.120989 -0.800606
#' # z.4 0.079422 -0.086063 0.180392
#' #
#' # ------------------------------------------------------
#' # * Confidence intervals computed by the basic
#' # bootstrap method, with 30 replications.
#' # ------------------------------------------------------
#'
#' @export
summary.PHcure <- function(object,conf.int = c("basic","percentile"),
conf.int.level = 0.95,...){
conf.int <- match.arg(conf.int)
digits <- 6
if (is.null(object$inference)){
table_CURE <- round(as.matrix(object$b),digits)
table_SURV <- round(as.matrix(object$beta),digits)
colnames(table_SURV) <- colnames(table_CURE) <- c("Estimate")
conf.int <- "no"
nboot <- 0
} else {
alpha <- 1 - conf.int.level
nboot <- object$inference$nboot
perc_boot_CURE <- t(apply(object$inference$bs,2,
quantile,c(alpha/2,1-alpha/2)))
perc_boot_SURV <- t(apply(object$inference$betas,2,
quantile,c(alpha/2,1-alpha/2)))
if (conf.int=="percentile"){
table_CURE <- round(cbind(object$b,perc_boot_CURE),digits)
table_SURV <- round(cbind(object$beta,perc_boot_SURV),digits)
} else if (conf.int=="basic"){
table_CURE <- round(cbind(object$b,
2*object$b-perc_boot_CURE[,2],
2*object$b-perc_boot_CURE[,1]),digits)
table_SURV <- round(cbind(object$beta,
2*object$beta-perc_boot_SURV[,2],
2*object$beta-perc_boot_SURV[,1]),digits)
}
colnames(table_SURV) <-
colnames(table_CURE) <- c("Estimate",
paste(alpha/2*100,"%",sep=''),
paste((1-alpha/2)*100,"%",sep=''))
}
rownames(table_CURE) <- names(object$b)
rownames(table_SURV) <- names(object$beta)
output <- list(N = object$N,
censoring = object$censoring,
K = object$K,
isTies = object$isTies,
conf.int = conf.int,
conf.int.level = conf.int.level,
nboot = nboot,
logL = object$logL,
CURE = table_CURE,
SURV = table_SURV)
class(output) <- 'summary.PHcure'
return(output)
}
a-beretta/penPHcure documentation built on Dec. 3, 2019, 5:41 p.m.
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Defines functions summary.PHcure
Documented in summary.PHcure
# ------------------------------------------------------------------------------
# Copyright (C) 2019 University of Liège
# <penPHcure is an R package for for estimation, variable selection and
# simulation of the semiparametric proportional-hazards (PH) cure model with
# time-varying covariates.>
# Authors: Alessandro Beretta & Cédric Heuchenne
# Contact: a.beretta@uliege.be
#
# Licence as published by the Free Software Foundation, either version 3 of the
# Licence, or any later version. This program is distributed in the hope that it
# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public Licence for more details. You should have received a copy of the GNU
# General Public Licence along with this program.
# If not, see <http://www.gnu.org/licenses/>.
# ------------------------------------------------------------------------------
#
#' @title Summary method for PHcure.object
#' @description Produces a summary of a fitted PH cure model
#' @param object an object of class \code{\link{PHcure.object}}.
#' @param conf.int a character string indicating the method to compute bootstrapped confidence intervals: \code{"percentile"} or \code{"basic"}. By default \code{conf.int = "basic"}.
#' @param conf.int.level confidence level. By default \code{conf.int.level = 0.95}.
#' @param ... ellipsis to pass extra arguments.
#' @usage
#' \method{summary}{PHcure}(object, conf.int = c("basic","percentile"), conf.int.level = 0.95,...)
#' @return An object of class \code{summary.PHcure}, a list including the following elements:
#' \item{\code{N}}{the sample size (number of individuals).}
#' \item{\code{censoring}}{the proportion of censored individuals.}
#' \item{\code{K}}{the number of unique failure times.}
#' \item{\code{isTies}}{a logical value, equal to \code{TRUE} in case of tied event times.}
#' \item{\code{conf.int}}{a character string indicating the method used to compute the bootstrapped confidence intervals: \code{"percentile"}, \code{"basic"} or \code{"no"}. The latter is returned when the \code{\link{penPHcure}} function was called with the argument \code{inference = FALSE}.}
#' \item{\code{conf.int.level}}{confidence level used to compute the bootstrapped confidence intervals.}
#' \item{\code{nboot}}{the number of bootstrap resamples for the construction of the confidence intervals.}
#' \item{\code{logL}}{the value of the log-likelihood for the estimated model.}
#' \item{\code{CURE}}{a matrix with one column containing the estimated regression coefficients in the incidence (cure) component. In case the function \code{\link{penPHcure}} was called with the argument \code{inference = TRUE}, two additional columns for the confidence intervals are provided.}
#' \item{\code{SURV}}{a matrix where in the first column the estimated regression coefficients in the latency (survival) component. In case the function \code{\link{penPHcure}} was called with the argument \code{inference = TRUE}, two additional columns for the confidence intervals are provided.}
#'
#' @examples
#' # For reproducibility
#' set.seed(12)
#' # If you use R v3.6 or greater, uncomment the following line
#' # RNGkind(sample.kind="Rounding")
#'
#' # Generate some data (for more details type ?penPHcure.simulate in your console)
#' data <- penPHcure.simulate(N=250)
#'
#' # Fit standard cure model (without inference)
#' fit <- penPHcure(Surv(time = tstart,time2 = tstop,
#' event = status) ~ z.1 + z.2 + z.3 + z.4,
#' cureform = ~ x.1 + x.2 + x.3 + x.4,data = data)
#'
#' # Use the summary method to see the results
#' summary(fit)
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficients +++
#' # ------------------------------------------------------
#' # Estimate
#' # (Intercept) 0.889668
#' # x.1 -0.972653
#' # x.2 -0.051580
#' # x.3 0.714611
#' # x.4 0.156169
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficients +++
#' # ------------------------------------------------------
#' # Estimate
#' # z.1 0.996444
#' # z.2 -0.048792
#' # z.3 -1.013562
#' # z.4 0.079422
#'
#' # Fit standard cure model (with inference). nboot = 30 bootstrap resamples
#' # are used to compute the confidence intervals.
#' fit2 <- penPHcure(Surv(time = tstart,time2 = tstop,
#' event = status) ~ z.1 + z.2 + z.3 + z.4,
#' cureform = ~ x.1 + x.2 + x.3 + x.4,data = data,
#' inference = TRUE,print.details = FALSE,nboot = 30)
#' # Use the summary method to see the results
#' summary(fit2)
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # (Intercept) 0.889668 0.455975 1.092495
#' # x.1 -0.972653 -1.414194 -0.503824
#' # x.2 -0.051580 -0.557843 0.304632
#' # x.3 0.714611 0.206211 1.081819
#' # x.4 0.156169 -0.011555 0.464841
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # z.1 0.996444 0.750321 1.130650
#' # z.2 -0.048792 -0.204435 0.073196
#' # z.3 -1.013562 -1.127882 -0.780339
#' # z.4 0.079422 -0.100677 0.193522
#' #
#' # ------------------------------------------------------
#' # * Confidence intervals computed by the basic
#' # bootstrap method, with 30 replications.
#' # ------------------------------------------------------
#'
#' # By default, confidence intervals are computed by the basic bootstrap method.
#' # Otherwise, the user can specify the percentile bootstrap method.
#' summary(fit2,conf.int = "percentile")
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # (Intercept) 0.889668 0.686842 1.323362
#' # x.1 -0.972653 -1.441483 -0.531112
#' # x.2 -0.051580 -0.407791 0.454684
#' # x.3 0.714611 0.347404 1.223011
#' # x.4 0.156169 -0.152503 0.323893
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficient estimates +++
#' # +++ and 95% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 2.5% 97.5%
#' # z.1 0.996444 0.862238 1.242567
#' # z.2 -0.048792 -0.170779 0.106852
#' # z.3 -1.013562 -1.246785 -0.899242
#' # z.4 0.079422 -0.034678 0.259521
#' #
#' # ------------------------------------------------------
#' # * Confidence intervals computed by the percentile
#' # bootstrap method, with 30 replications.
#' # ------------------------------------------------------
#'
#' # By default, a 95% confidence level is used. Otherwise, the user can specify
#' # another confidence level: e.g. 90%.
#' summary(fit2,conf.int.level = 0.90)
#' #
#' # ------------------------------------------------------
#' # +++ PH cure model with time-varying covariates +++
#' # ------------------------------------------------------
#' # Sample size: 250
#' # Censoring proportion: 0.5
#' # Number of unique event times: 125
#' # Tied failure times: FALSE
#' #
#' # log-likelihood: 74.11
#' #
#' # ------------------------------------------------------
#' # +++ Cure (incidence) coefficient estimates +++
#' # +++ and 90% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 5% 95%
#' # (Intercept) 0.889668 0.467864 1.074423
#' # x.1 -0.972653 -1.397088 -0.618265
#' # x.2 -0.051580 -0.527389 0.249460
#' # x.3 0.714611 0.314140 1.028425
#' # x.4 0.156169 0.033802 0.436361
#' #
#' # ------------------------------------------------------
#' # +++ Survival (latency) coefficient estimates +++
#' # +++ and 90% confidence intervals * +++
#' # ------------------------------------------------------
#' # Estimate 5% 95%
#' # z.1 0.996444 0.767937 1.125745
#' # z.2 -0.048792 -0.158821 0.050965
#' # z.3 -1.013562 -1.120989 -0.800606
#' # z.4 0.079422 -0.086063 0.180392
#' #
#' # ------------------------------------------------------
#' # * Confidence intervals computed by the basic
#' # bootstrap method, with 30 replications.
#' # ------------------------------------------------------
#'
#' @export
summary.PHcure <- function(object,conf.int = c("basic","percentile"),
conf.int.level = 0.95,...){
conf.int <- match.arg(conf.int)
digits <- 6
if (is.null(object$inference)){
table_CURE <- round(as.matrix(object$b),digits)
table_SURV <- round(as.matrix(object$beta),digits)
colnames(table_SURV) <- colnames(table_CURE) <- c("Estimate")
conf.int <- "no"
nboot <- 0
} else {
alpha <- 1 - conf.int.level
nboot <- object$inference$nboot
perc_boot_CURE <- t(apply(object$inference$bs,2,
quantile,c(alpha/2,1-alpha/2)))
perc_boot_SURV <- t(apply(object$inference$betas,2,
quantile,c(alpha/2,1-alpha/2)))
if (conf.int=="percentile"){
table_CURE <- round(cbind(object$b,perc_boot_CURE),digits)
table_SURV <- round(cbind(object$beta,perc_boot_SURV),digits)
} else if (conf.int=="basic"){
table_CURE <- round(cbind(object$b,
2*object$b-perc_boot_CURE[,2],
2*object$b-perc_boot_CURE[,1]),digits)
table_SURV <- round(cbind(object$beta,
2*object$beta-perc_boot_SURV[,2],
2*object$beta-perc_boot_SURV[,1]),digits)
}
colnames(table_SURV) <-
colnames(table_CURE) <- c("Estimate",
paste(alpha/2*100,"%",sep=''),
paste((1-alpha/2)*100,"%",sep=''))
}
rownames(table_CURE) <- names(object$b)
rownames(table_SURV) <- names(object$beta)
output <- list(N = object$N,
censoring = object$censoring,
K = object$K,
isTies = object$isTies,
conf.int = conf.int,
conf.int.level = conf.int.level,
nboot = nboot,
logL = object$logL,
CURE = table_CURE,
SURV = table_SURV)
class(output) <- 'summary.PHcure'
return(output)
}
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