R: Plot power curves for survival analysis with time-dependent...
plot_power
R Documentation
Plot power curves for survival analysis with time-dependent exposure
Description
This function plots a power curve at each time and returns a subsetted data
frame that match the list of input parameters.
Usage
plot_power(table_df, N, type, exp.prop, min.futime, min.postexp.futime,
show.plot = FALSE, newplot = FALSE, col = NULL, lty, lwd, pch)
Arguments
table_df
A data frame read from a .csv file of a format output from the getpower.method1 function.
N
Number of subjects needs to be screened
type
A text string indicating the what type of dataset is of interest. Either one of
"fixed" or "td" should be inputted
exp.prop
A numeric value between 0 and 1 (not include 0 and 1) that represents the proportion of subjects that are assigned with an exposure
min.futime
A numeric value that represents minimum follow-up time (in months). The default value is 0, which means no minimum follow-up time is considered. If it has a positive value, this argument will help exclude subjects that only spend a short amount of time in the study
min.postexp.futime
A numeric value that represents minimum post-exposure follow-up time (in months). The default value is 0, which means no minimum post-exposure follow-up time is considered. If it has a positive value, this argument will help exclude subjects that only spend a short amount of time in the study after their exposure
show.plot
A logical value indicating whether to output a power curve or not. The default value is TRUE
newplot
A logical value indicating whether to create a new plot or add to an existing plot
col,lty,lwd,pch
Graphical parameters as in the regular plot function in R
Details
The gist of this function is that the user can check the plot with the values in the output data frame. Moreover, it is flexible that the user can choose to only output the data frame in order to plot their own graph (such as add titles or legends) based on the data. The user can also add as many lines as possible to an existing power curve plot so that he/she is allowed to compare different scenarios.
Value
A data.frame object with columns corresponding to
i_N
Number of subjects needs to be screened, specified by the user
N_eff
Simulated number of evaluable subjects, which is the resulting number of subjects with or without considering minimum follow-up time and/or minimum post-exposure follow-up time
i_beta
Input value of regression coefficient (log hazard ratio)
pow
Simulated statistical power from the Cox regression model on data with time-dependent exposure
# We recommend setting nSim to at least 500. It is set to 10 in the example to
# reduce run time for CRAN submission.
ret <- getpower.method1(nSim = 10, N = 600, b = 0.3, exp.prop = 0.2,
type = "td", scenario = " ", maxrelexptime = 1/6, min.futime = 4,
min.postexp.futime = 4, output.fn = "output.csv")
ret2 <- getpower.method1(nSim = 10, N = 600, b = 0.3, exp.prop = 0.2,
type = "td", scenario = " ", maxrelexptime = 1/6, min.futime = 4,
min.postexp.futime = 0, output.fn ="output.csv")
# Read in .csv file as a data frame
tb <- read.csv("output.csv", header = TRUE, sep = ",")
# Visualize the subsetted data frame of interest and create a new plot
visualize1 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop = 0.2,
min.futime = 4, min.postexp.futime = 4, show.plot = TRUE, newplot = TRUE,
col = "red", lty = 1, lwd = 2, pch = 16)
# Add a different power curve to the previously created plot
visualize2 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop=0.2,
min.futime = 4, min.postexp.futime = 0, show.plot = TRUE, newplot = FALSE,
col = "blue", lty = 1, lwd = 2, pch = 16)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(SimHaz)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SimHaz/plot_power.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot_power
> ### Title: Plot power curves for survival analysis with time-dependent
> ### exposure
> ### Aliases: plot_power
> ### Keywords: Plot
>
> ### ** Examples
>
> # We recommend setting nSim to at least 500. It is set to 10 in the example to
> # reduce run time for CRAN submission.
>
> ret <- getpower.method1(nSim = 10, N = 600, b = 0.3, exp.prop = 0.2,
+ type = "td", scenario = " ", maxrelexptime = 1/6, min.futime = 4,
+ min.postexp.futime = 4, output.fn = "output.csv")
>
> ret2 <- getpower.method1(nSim = 10, N = 600, b = 0.3, exp.prop = 0.2,
+ type = "td", scenario = " ", maxrelexptime = 1/6, min.futime = 4,
+ min.postexp.futime = 0, output.fn ="output.csv")
>
> # Read in .csv file as a data frame
>
> tb <- read.csv("output.csv", header = TRUE, sep = ",")
>
> # Visualize the subsetted data frame of interest and create a new plot
>
> visualize1 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop = 0.2,
+ min.futime = 4, min.postexp.futime = 4, show.plot = TRUE, newplot = TRUE,
+ col = "red", lty = 1, lwd = 2, pch = 16)
>
> # Add a different power curve to the previously created plot
>
> visualize2 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop=0.2,
+ min.futime = 4, min.postexp.futime = 0, show.plot = TRUE, newplot = FALSE,
+ col = "blue", lty = 1, lwd = 2, pch = 16)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.