plot.bayes_ctd_array {BayesCTDesign}  R Documentation 
Plot Data from Two Arm Bayesian Clinical Trial Simulation.
Description
plot.bayes_ctd_array()
takes an S3 object of class bayes_ctd_array
, and
creates a line plot from a one or two dimensional slice of the data generated by a
clinical trial simulation using historic_sim()
or simple_sim()
. The
plotted results can be smoothed or unsmoothed.
Usage
## S3 method for class 'bayes_ctd_array'
plot(
x = NULL,
measure = "power",
tab_type = "WXYZ",
smooth = FALSE,
plot_out = TRUE,
subj_per_arm_val = NULL,
a0_val = NULL,
effect_val = NULL,
rand_control_diff_val = NULL,
span = 0.75,
degree = 2,
family = "gaussian",
title = NULL,
ylim = NULL,
...
)
Arguments
x 
Name of object of class 
measure 
Must be equal to 
tab_type 
A character string that must equal 
smooth 
A true/false parameter indicating whether smoothed results
should be plotted. Note, smoothing of simulation results requires the length of

plot_out 
A true/false parameter indicating whether the plot should be
produced. This parameter is useful if the user only wants a table of smoothed
values. Default is 
subj_per_arm_val 
Must be nonmissing, if 
a0_val 
Must be nonmissing, if 
effect_val 
Must be nonmissing, if 
rand_control_diff_val 
Must be nonmissing, if 
span 
The 
degree 
The 
family 
The 
title 
Title for the plot. 
ylim 
Lower and upper limits for yaxis of plot. 
... 
further arguments passed to or from other methods. 
Details
If the object of class bayes_ctd_array
is created by historic_sim()
,
the function plot()
allows the user to create line plots of userspecified
1 or 2 dimensional slices of the simulation results based on slicing code
described below. If the object of class bayes_ctd_array
is created by
simple_sim()
, a basic plot of characteristic by sample size and effect is created.
If the object of class bayes_ctd_array
is created by simple_sim()
, then
all four trial characteristics (subj_per_arm_val
, a0_vals
,
effect_val
, and rand_control_diff_val
) can be ignored as can the
parameter defining what type of plot to create through the parameter tab_type
.
A call to plot()
will require the user to specify a measure (power, est,
var, bias, or mse).
If the object of class bayes_ctd_array
is created by historic_sim()
,
when calling plot()
the user must specify a measure to plot
(power, est, var, bias, or mse) and may be required to specify a plot type through
the tab_type
parameter. A plot type, tab_type
, will be required if
3 of the 4 trial characteristics are equal to a vector of 2 or more values. This
plot type specification uses the letters W, X, Y, and Z. The letter W represents
the subject per arm dimension. The letter X represents the a0 dimension. The
letter Y represents the effect dimension. The letter Z represents the control
difference dimension. To plot a slice of the 4dimensional array, these letters
are put into an ABCD pattern just like in print()
. The two letters
to the right of the vertical bar define which variables are held constant. The two
letters to the left of the vertical bar define which variables are going to show up
in the plot. The first letter defines the xaxis variable and the second letter
defines the stratification variable. The result is a plot of power, estimate,
variance, bias, or mse by the trial characteristic represented by the first letter.
On this plot, one line will be created for each value of the trial characteristic
represented by the second letter. For example if tab_type equals WXYZ
,
then effect and control differences will be held constant, while sample size will be
represented along the horizontal axis and a0 values will be represented by separate
lines. The actual values that are plotted on the yaxis depend on what measure is
requested in the parameter measure
.

tab_type='WXYZ'
, Sample Size by a0 
tab_type='WYXZ'
, Sample Size by Effect 
tab_type='WZXY'
, Sample Size by Control Differences 
tab_type='XYWZ'
, a0 by Effect 
tab_type='XZWY'
, a0 by Control Differences 
tab_type='YZWX'
, Effect by Control Differences 
tab_type='ZXWY'
, Control Differences by a0 
tab_type='XWYZ'
, a0 by Sample Size 
tab_type='YWXZ'
, Effect by Sample Size 
tab_type='YXWZ'
, Effect by a0 
tab_type='ZWXY'
, Control Differences by Sample Size 
tab_type='ZYWX'
, Control Differences by Effect
It is very important to populate the values of subj_per_arm_val
,
a0_val
, effect_val
, and rand_control_diff_val
correctly given
the value of tab_type, when the object of class bayes_ctd_array
is created by
historic_sim()
and at least 3 of the four parameters have more than one
value. On, the other hand, if 2 or more of the four parameters have only one value,
then subj_per_arm_val
, a0_vals
, effect_val
,
rand_control_diff_val
, as well as tab_type
can be ignored. If the last
two letters are YZ
, then effect_val
and rand_control_diff_val
must be populated. If the last two letters are XZ
, then a0_val
and
rand_control_diff_val
must be populated. If the last two letters are XY
,
then a0_val
and effect_val
must be populated. If the last two letters
are WZ
, then sample_val
and rand_control_diff_val
must be
populated. If the last two letters are WY
, then sample_size_val
and
effect_val
must be populated. If the last two letters are WX
, then
sample_size_val
and a0_val
must be populated.
If the object of class bayes_ctd_array
is created by simple_sim()
, the
parameters tab_type
, subj_per_arm_val
, a0_val
, effect_val
,
and rand_control_diff_val
are ignored.
Value
plot()
returns a plot for a two dimensional array of simulation
results. If smooth
is TRUE
, then the plot is based on a smoothed
version of the simulation results. If smooth
is FALSE
, then the plot
is based on the raw data from the simulation results. What actually is plotted
depends on the value of measure
. If plot_out
is FALSE
, the
plot is not created. This option is useful when the user wants a table of smoothed
simulation results but does not want the plot. Smoothing of simulation results
requires the length of subj_per_arm_val
or a0_val
or effect_val
or rand_control_diff_val
, whichever populates the xaxis on the graph to
contain enough elements to justify the smoothing. No checking occurs to
determine if enough elements are present to justify smoothing.
Examples
#Run a Weibull simulation, using simple_sim().
#For meaningful results, trial_reps needs to be much larger than 2.
weibull_test < simple_sim(trial_reps = 2, outcome_type = "weibull",
subj_per_arm = c(50, 100, 150, 200),
effect_vals = c(0.6, 1),
control_parms = c(2.82487,3), time_vec = NULL,
censor_value = NULL, alpha = 0.05,
get_var = TRUE, get_bias = TRUE, get_mse = TRUE,
seedval=123, quietly=TRUE)
#Create a plot of the power simulation results.
plot(x=weibull_test, measure="power", tab_type=NULL,
smooth=FALSE, plot_out=TRUE, subj_per_arm_val=NULL, a0_val=NULL,
effect_val=NULL, rand_control_diff_val=NULL)
#Create a plot of the hazard ratio simulation results.
plot(x=weibull_test, measure="est", tab_type=NULL,
smooth=FALSE, plot_out=TRUE, subj_per_arm_val=NULL, a0_val=NULL,
effect_val=NULL, rand_control_diff_val=NULL)
#Create a plot of the hazard ratio variance simulation results.
plot(x=weibull_test, measure="var", tab_type=NULL,
smooth=FALSE, plot_out=TRUE, subj_per_arm_val=NULL, a0_val=NULL,
effect_val=NULL, rand_control_diff_val=NULL)
#Create a plot of the hazard ratio bias simulation results.
plot(x=weibull_test, measure="bias", tab_type=NULL,
smooth=FALSE, plot_out=TRUE, subj_per_arm_val=NULL, a0_val=NULL,
effect_val=NULL, rand_control_diff_val=NULL)
#Create a plot of the hazard ratio mse simulation results.
plot(x=weibull_test, measure="mse", tab_type=NULL,
smooth=FALSE, plot_out=TRUE, subj_per_arm_val=NULL, a0_val=NULL,
effect_val=NULL, rand_control_diff_val=NULL)
#Run a second Weibull simulation, using simple_sim() and smooth the plot.
#For meaningful results, trial_reps needs to be larger than 100.
weibull_test2 < simple_sim(trial_reps = 100, outcome_type = "weibull",
subj_per_arm = c(50, 75, 100, 125, 150, 175, 200, 225, 250),
effect_vals = c(0.6, 1, 1.4),
control_parms = c(2.82487,3), time_vec = NULL,
censor_value = NULL, alpha = 0.05, get_var = TRUE,
get_bias = TRUE, get_mse = TRUE, seedval=123,
quietly=TRUE)
#Create a plot of the power simulation results.
plot(x=weibull_test2, measure="power", tab_type=NULL,
smooth=TRUE, plot_out=TRUE, subj_per_arm_val=NULL, a0_val=NULL,
effect_val=NULL, rand_control_diff_val=NULL, span=c(1,1,1))
#Run a third weibull simulation, using historic_sim().
#Note: historic_sim() can take a while to run.
#Generate a sample of historical data for use in example.
set.seed(2250)
SampleHistData < genweibulldata(sample_size=60, scale1=2.82487,
hazard_ratio=0.6, common_shape=3,
censor_value=3)
histdata < subset(SampleHistData, subset=(treatment==0))
histdata$id < histdata$id+10000
#For meaningful results, trial_reps needs to be larger than 100.
weibull_test3 < historic_sim(trial_reps = 100, outcome_type = "weibull",
subj_per_arm = c(50, 100, 150, 200, 250),
a0_vals = c(0, 0.33, 0.67, 1),
effect_vals = c(0.6, 1, 1.4),
rand_control_diff = c(0.8, 1, 1.2),
hist_control_data = histdata, time_vec = NULL,
censor_value = 3, alpha = 0.05, get_var = TRUE,
get_bias = TRUE, get_mse = TRUE, seedval=123,
quietly=TRUE)
#Create a plot of the power simulation results.
plot(x=weibull_test3, measure="power", tab_type="WXYZ",
smooth=FALSE, plot_out=TRUE, effect_val=0.6,
rand_control_diff_val=1.0)
#Run a Gaussian simulation, using historic_sim()
#Generate a sample of historical Gaussian data for use in example.
set.seed(2250)
samplehistdata < gengaussiandata(sample_size=60, mu1=25, mean_diff=0, common_sd=3)
histdata < subset(samplehistdata, subset=(treatment==0))
histdata$id < histdata$id+10000
#For meaningful results, trial_reps needs to be larger than 100.
gaussian_test < historic_sim(trial_reps = 100, outcome_type = "gaussian",
subj_per_arm = c(150),
a0_vals = c(1.0),
effect_vals = c(0.15),
rand_control_diff = c(4.0,3.5,3.0,2.5,2.0,
1.5,1.0,0.5,0,0.5,1.0),
hist_control_data = histdata, time_vec = NULL,
censor_value = 3, alpha = 0.05, get_var = TRUE,
get_bias = TRUE, get_mse = TRUE, seedval=123,
quietly=TRUE)
test_table < print(x=gaussian_test, measure="power",
tab_type=NULL, effect_val=NULL,
subj_per_arm_val=NULL)
print(test_table)
#Create a plot of the power simulation results.
plot(x=gaussian_test, measure="power", tab_type=NULL,
smooth=TRUE, plot_out=TRUE, effect_val=NULL,
rand_control_diff_val=NULL)
#Generate a sample of historical pwe data for use in example.
set.seed(2250)
nvalHC < 60
time.vec < c(0.3,0.9,1.5,2.1,2.4)
lambdaHC.vec < c(0.19,0.35,0.56,0.47,0.38,0.34)
censor.value < 3
SampleHistData < genpwedata(nvalHC, lambdaHC.vec, 1.0, time.vec, censor.value)
histdata < subset(SampleHistData, subset=(treatment==0))
histdata$indicator < 2 #If set to 2, then historical controls will be collapsed with
#randomized controls, when time_vec is reconsidered and
#potentially restructured. If set to 1, then historical
#controls will be treated as a separated cohort when
#time_vec is being assessed for restructuring.
histdata$id < histdata$id+10000
#Run a pwe simulation, using historic_sim().
#For meaningful results, trial_reps needs to be larger than 100.
pwe_test < historic_sim(trial_reps = 100, outcome_type = "pwe",
subj_per_arm = c(25,50,75,100,125,150,175,200,225,250),
a0_vals = c(1.0),
effect_vals = c(0.6),
rand_control_diff = c(1.8),
hist_control_data = histdata, time_vec = time.vec,
censor_value = 3, alpha = 0.05, get_var = TRUE,
get_bias = TRUE, get_mse = TRUE, seedval=123,
quietly=TRUE)
#Create a plot of the power simulation results.
plot(x=pwe_test, measure="power", tab_type=NULL,
smooth=TRUE, plot_out=TRUE, effect_val=NULL,
rand_control_diff_val=NULL)