gestate (version 1.3.2)

plot_npht: Plot output from nph_traj

Description

This function plots the output from nph_traj. By default, it produces 6 plots:

• "KM plot" Kaplan Meier plot for events. This is in patient time.

• "Censoring plot" Plot of CDFs for censoring functions. This is in patient time.

• "Recruitment plot" Number of patients expected to have been recruited over time. This is in trial time.

• "Event plot" Total number of events expected to occur over time. This is in trial time.

• "log(HR) plot" Expected log(HR), with expected confidence interval, over time. This is in trial time.

• "Power plot" Expected power over time for various methods. This is in trial time.

Plots may be omitted via arguments. All calculated powers automatically plotted unless specified otherwise.

Usage

plot_npht(data, KM = TRUE, censor = TRUE, recruitment = TRUE,
  events = TRUE, logHR = TRUE, power = TRUE,
  include_frontier = TRUE, include_RMST = TRUE,
  include_landmark = TRUE, alpha1 = 0.025,
  legend_position = c("top_left", "top_right", "bottom_right"))

Arguments

data

Full output list from nph_curve_trajectories

KM

Boolean to include KM plot (Default = TRUE)

censor

Boolean to include censoring plot (Default = TRUE)

recruitment

Boolean to include recruitment plot (Default = TRUE)

events

Boolean to include events plot (Default = TRUE)

logHR

Boolean to include log(HR) plot (Default = TRUE)

power

Boolean to include power plot (Default = TRUE)

include_frontier

Boolean to include frontier power curve in power plot (Default = TRUE)

include_RMST

Boolean to include RMST power curve in power plot if available (Default = TRUE)

include_landmark

Boolean to include landmark power curve in power plot if available (Default = TRUE)

alpha1

One-sided alpha to use for estimation of log(HR) confidence intervals (Default = 0.025)

legend_position

String with any of "top_left","top_right" or "bottom_right", corresponding to legend position in power plot. Default is "top_left".

Value

Returns NULL

Examples

# NOT RUN {
trial <- nph_traj(Weibull(100,1),Weibull(70,1),rcurve=LinearR(12,100,100),RMST=20,landmark=20,
max_assessment=30)

plot_npht(trial)
plot_npht(data=trial,KM=FALSE,censor=FALSE,recruitment=FALSE)
plot_npht(data=trial,KM=FALSE,censor=FALSE,recruitment=FALSE,events=FALSE,logHR=FALSE,
include_frontier=FALSE, include_RMST=FALSE,include_landmark=FALSE,legend_position="top_right")

# }