.Rhistory

rho,
rho_type='Spearman',
followup_time=1,
alpha=0.05,
power=0.80 ,
ss_formula='schoenfeld')
plot_tte(p0_e1,
p0_e2,
HR_e1,
HR_e2,
beta_e1,
beta_e2,
case,
copula = 'Frank',
rho,
rho_type='Spearman',
followup_time=1,
alpha=0.05,
power=0.80 ,
ss_formula='schoenfeld')
plot_tte(p0_e1,
p0_e2,
HR_e1,
HR_e2,
beta_e1,
beta_e2,
case,
copula = 'Frank',
rho,
rho_type='Spearman',
followup_time=1,
alpha=0.05,
power=0.80 ,
ss_formula='schoenfeld')
plot_surv <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE, plot_save =TRUE)$gg_object
plot_surv
install.packages("CompAREdesign")
install.packages("CompAREdesign", dependencies = TRUE)
sessionInfo()
remove.packages("CompAREdesign")
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho, rho_type='Spearman',
followup_time=1,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
## Probabilities of observing the event in control arm during follow-up
p0_e1 <- 0.59   # Death
p0_e2 <- 0.74   # Disease Progression
## Effect size (Cause specific hazard ratios) for each endpoint
HR_e1 <- 0.91   # Death
HR_e2 <- 0.77   # Disease Progression
## Hazard rates over time
beta_e1 <- 2    # Death --> Increasing risk over time
beta_e2 <- 1    # Disease Progression --> Constant risk over time
## Correlation
rho      <- 0.1        # Correlation between components
rho_type <- 'Spearman' # Type of correlation measure
copula   <- 'Frank'    # Copula used to get the joint distribution
## Additional parameter
case  <- 3  # 1: No deaths;                  2: Death is the secondary event;
# 3: Death is the primary event; 4: Both events are death by different causes
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho, rho_type='Spearman',
followup_time=1,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho, rho_type='Spearman',
followup_time=1,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho, rho_type='Spearman',
followup_time=1,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
followup_time<- 1
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=TRUE,plot_save=TRUE)
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
#' Plot graphics related to the composite endpoint.
#'
#' @description Plot the survival function and the HR for composite endpoint over time and the ARE (Assymptotic Relative Efficiency) and sample size
#' size according to the correlation.The composite endpoint is assumed to be a time to event endpoint formed by a combination of two events (E1 and E2). We assume that the endpoint 1 is more relevant for the clinical question than endpoint 2.
#' #'
#'
#' @param p0_e1 numeric parameter between 0 and 1, expected proportion of observed events for the endpoint E1
#' @param p0_e2 numeric parameter between 0 and 1, expected proportion of observed events for the endpoint E2
#' @param HR_e1 numeric parameter between 0 and 1, expected cause specific hazard Ratio the endpoint E1
#' @param HR_e2 numeric parameter between 0 and 1, expected cause specific hazard Ratio the endpoint E2
#' @param beta_e1 numeric positive parameter, shape parameter (\eqn{\beta_1}) for a Weibull distribution for the endpoint E1 in the control group. See details for more info.
#' @param beta_e2 numeric positive parameter, shape parameter (\eqn{\beta_2}) for a Weibull distribution for the endpoint E2 in the control group. See details for more info.
#' @param case integer parameter in \{1,2,3,4\}: (1) none of the endpoints is death; (2) endpoint 2 is death; (3) endpoint 1 is death; (4) both endpoints are death by different causes.
#' @param copula character indicating the copula to be used: "Frank" (default), "Gumbel" or "Clayton". See details for more info.
#' @param rho numeric parameter between -1 and 1, Spearman's correlation coefficient o Kendall Tau between the marginal distribution of the times to the two events E1 and E2. See details for more info.
#' @param rho_type character indicating the type of correlation to be used: "Spearman" (default) or "Tau". See details for more info.
#' @param followup_time numeric parameter indicating the maximum follow up time (in any unit). Default is 1.
#' @param alpha numeric parameter. The probability of type I error. By default \eqn{\alpha=0.05}
#' @param power numeric parameter. The power to detect the treatment effect. By default \eqn{1-\beta=0.80}
#' @param ss_formula character indicating the formula to be used for the sample size calculation on the single components: 'schoenfeld' (default) or 'freedman'
#'
#' @import ggpubr
#' @export
#'
#' @return Four plots related to composite endpoint are returned:
#' \describe{
#' \item{S}{Survival curve for the composite endpoint over time}
#' \item{HR}{Hazard Ratio for the composite endpoint over time}
#' \item{ARE}{ARE according to correlation (\eqn{\rho})}
#' \item{SS}{Sample size for the composite endpoint according to correlation (\eqn{\rho})}
#' }
#'
#' @details Some parameters might be difficult to anticipate, especially the shape parameters of Weibull distributions and those referred to the relationship between the marginal distributions.
#' For the shape parameters (beta_e1, beta_e2) of the Weibull distribution, we recommend to use \eqn{\beta_j=0.5}, \eqn{\beta_j=1} or \eqn{\beta_j=2} if a decreasing, constant or increasing rates over time are expected, respectively.
#' For the correlation (rho) between both endpoints, generally a positive value is expected as it has no sense to design an study with two endpoints negatively correlated. We recommend to use \eqn{\rho=0.1}, \eqn{\rho=0.3} or \eqn{\rho=0.5} for weak, mild and moderate correlations, respectively.
#' For the type of correlation (rho_type), although two different type of correlations are implemented, we recommend the use of the Spearman's correlation.
#' In any case, if no information is available on these parameters, we recommend to use the default values provided by the function.
#'
#'
#'
plot_tte <- function(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time=1,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld'){
requireNamespace("stats")
if(p0_e1 < 0 || p0_e1 > 1){
stop("The probability of observing the event E1 (p_e1) must be a number between 0 and 1")
}else if(p0_e2 < 0 || p0_e2 > 1){
stop("The probability of observing the event E2 (p_e2) must be a number between 0 and 1")
}else if(HR_e1 < 0 || HR_e1 > 1){
stop("The hazard ratio for the relevant endpoint E1 (HR_e1) must be a number between 0 and 1")
}else if(HR_e2 < 0 || HR_e2 > 1){
stop("The hazard ratio for the secondary endpoint E2 (HR_e2) must be a number between 0 and 1")
}else if(beta_e1 <= 0){
stop("The shape parameter for the marginal weibull distribution of the relevant endpoint E1 (beta_e1) must be a positive number")
}else if(beta_e2 <= 0){
stop("The shape parameter for the marginal weibull distribution of the secondary endpoint E2 (beta_e2) must be a positive number")
}else if(!case %in% 1:4){
stop("The case (case) must be a number in {1,2,3,4}. See ?ARE_tte")
}else if(!copula %in% c('Frank','Gumbel','Clayton')){
stop("The copula (copula) must be one of 'Frank','Gumbel' or 'Clayton'")
}else if(rho < -1 || rho > 1){
stop("The correlation (rho) must be a number between -1 and 1")
}else if(!rho_type %in% c('Spearman','Kendall')){
stop("The correlation type (rho_type) must be one of 'Spearman' or 'Kendall'")
}else if(case==4 && p0_e1 + p0_e2 > 1){
stop("The sum of the proportions of observed events in both endpoints in case 4 must be lower than 1")
}else if(!(is.numeric(followup_time) && followup_time>0)){
stop("The followup_time must be a positive numeric value")
}else if(alpha<=0 || alpha>=1){
stop("The probability of type I error (alpha) must be a numeric value between 0 and 1")
}else if(power<=0 || power>=1){
stop("The power must be a numeric value between 0 and 1")
}else if(!ss_formula %in% c('schoenfeld','freedman')){
stop("The selected formula (ss_formula) must be one of 'schoenfeld' (default) or 'freedman'")
}
invisible(capture.output(plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object))
invisible(capture.output(plot_effect <- effectsize_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object))
invisible(capture.output(plot_ARE    <- ARE_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
plot_res=FALSE,plot_save=TRUE)$gg_object))
invisible(capture.output(plot_ss     <- samplesize_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = copula, rho=rho, rho_type=rho_type,
plot_res=FALSE,plot_save=TRUE,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')$gg_object))
print(ggarrange(plot_surv,
plot_effect,
plot_ARE,
plot_ss,ncol=2,nrow=2))
}
detach("package:CompAREdesign", unload = TRUE)
library(CompAREdesign)
detach("package:CompAREdesign", unload = TRUE)
p0_e1 <- 0.59   # Death
p0_e2 <- 0.74   # Disease Progression
## Effect size (Cause specific hazard ratios) for each endpoint
HR_e1 <- 0.91   # Death
HR_e2 <- 0.77   # Disease Progression
## Hazard rates over time
beta_e1 <- 2    # Death --> Increasing risk over time
beta_e2 <- 1    # Disease Progression --> Constant risk over time
## Correlation
rho      <- 0.1        # Correlation between components
rho_type <- 'Spearman' # Type of correlation measure
copula   <- 'Frank'    # Copula used to get the joint distribution
## Additional parameter
case  <- 3  # 1: No deaths;                  2: Death is the secondary event;
# 3: Death is the primary event; 4: Both events are death by different causes
followup_time <- 1
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=TRUE,plot_save=TRUE)
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld'
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
followup_time <- 3
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
followup_time <- 1
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=1, beta_e2=1, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
followup_time <- 1
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
## Probabilities of observing the event in control arm during follow-up
p0_e1 <- 0.59   # Death
p0_e2 <- 0.74   # Disease Progression
## Effect size (Cause specific hazard ratios) for each endpoint
HR_e1 <- 0.91   # Death
HR_e2 <- 0.77   # Disease Progression
## Hazard rates over time
beta_e1 <- 2    # Death --> Increasing risk over time
beta_e2 <- 1    # Disease Progression --> Constant risk over time
## Correlation
rho      <- 0.1        # Correlation between components
rho_type <- 'Spearman' # Type of correlation measure
copula   <- 'Frank'    # Copula used to get the joint distribution
## Additional parameter
case  <- 3  # 1: No
followup_time <- 1
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
followup_time <- 3
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
followup_time <- 30
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
followup_time <- 1
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
plot_surv + theme_dark()
plot_surv + theme_bwk()
plot_surv + theme_bw()
a <- plot_surv + theme_bw()
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
theme_bw
plot_surv + theme_bw()
library(ggplot2)
plot_surv + theme_bw()
plot_tte + theme_bw()
plot_tte
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plots
plots + theme_bw()
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', tema = theme_bw())
plot_surv
plots
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', tema = theme_void())
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', tema = theme_tufte())
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', tema = theme_classic())
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', tema = theme_bw())
plots <- plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plots
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=TRUE,plot_save=TRUE)$gg_object
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', theme_bw())
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', title("prova"))
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', title ="prova")
rlang::last_trace()
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', labs(title ="prova"))
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', theme_bw())
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_surv   <- surv_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1=beta_e1, beta_e2=beta_e2, case=case,
copula = copula, rho=rho, rho_type=rho_type,
followup_time=followup_time,
plot_res=FALSE,plot_save=TRUE)$gg_object
plot_surv
rlang::last_trace()
rlang::last_trace(drop = FALSE)
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', theme_bw())
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', theme_bw(), labs(title = "prova"))
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', labs(title = "prova"))
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', labs(title = "prova"), theme_bw())
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', theme_bw())
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld', theme_bw())
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 ,ss_formula='schoenfeld')
## Probabilities of observing the event in control arm during follow-up
p0_e1 <- 0.59   # Death
p0_e2 <- 0.74   # Disease Progression
## Effect size (Cause specific hazard ratios) for each endpoint
HR_e1 <- 0.91   # Death
HR_e2 <- 0.77   # Disease Progression
## Hazard rates over time
beta_e1 <- 2    # Death --> Increasing risk over time
beta_e2 <- 1    # Disease Progression --> Constant risk over time
## Correlation
rho      <- 0.1        # Correlation between components
rho_type <- 'Spearman' # Type of correlation measure
copula   <- 'Frank'    # Copula used to get the joint distribution
## Additional parameter
case  <- 3  # 1: No deaths;                  2: Death is the secondary event;
# 3: Death is the primary event; 4: Both events are death by different causes
followup_time <- 1
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 , ss_formula='schoenfeld',
theme_bw(),
labs(title = "Composite Endpoint Analysis",
subtitle = "Sample Size and Efficiency"))
library(ggplot2)
plot_tte(p0_e1, p0_e2, HR_e1, HR_e2, beta_e1, beta_e2, case,
copula = 'Frank', rho=0.3, rho_type='Spearman',
followup_time,
alpha=0.05, power=0.80 , ss_formula='schoenfeld',
theme_bw(),
labs(title = "Composite Endpoint Analysis",
subtitle = "Sample Size and Efficiency"))
## Probabilities of observing the event in control arm during follow-up
p0_e1 <- 0.59   # Death
p0_e2 <- 0.74   # Disease Progression
## Effect size (Cause specific hazard ratios) for each endpoint
HR_e1 <- 0.91   # Death
HR_e2 <- 0.77   # Disease Progression
## Hazard rates over time
beta_e1 <- 2    # Death --> Increasing risk over time
beta_e2 <- 1    # Disease Progression --> Constant risk over time
## Correlation
rho      <- 0.1        # Correlation between components
rho_type <- 'Spearman' # Type of correlation measure
copula   <- 'Frank'    # Copula used to get the joint distribution
## Additional parameter
case  <- 3  # 1: No deaths;                  2: Death is the secondary event;
# 3: Death is the primary event; 4: Both events are death by different causes
eff_e1 <- -0.0196
eff_e2 <- -0.0098
effm_e1 <- "diff"
effm_e2 <- "diff"
samplesize <- 100
simula_cbe(p0_e1, p0_e2, eff_e1, effm_e1, eff_e2, effm_e2, rho, samplesize)
test <- simula_cbe(p0_e1, p0_e2, eff_e1, effm_e1, eff_e2, effm_e2, rho, samplesize)
test <- simula_cbe(p0_e1, p0_e2, eff_e1, effm_e1, eff_e2, effm_e2, rho, samplesize)
View(test)