Population time plots can be extremely informative graphical displays
of survival data. They should be the first step in your exploratory data
analyses. We facilitate this task in the casebase
package
by providing a plot
method for objects returned by the
popTime
function. This is done in two steps:
casebase::popTime
function and
specify the column names corresponding toThis will create an object of class popTime
with an
exposure attribute if you specify a value for the exposure
argument.
popTime
to the
plot
functionIn this vignette we show how to create population-time plots for the
two datasets that ship with the casebase
package, as well
as several well known survival datasets from the survival
package. For a more detailed description of how to modify the aesthetics
of a population time plot, please refer to the Customizing
Population Time Plots vignette.
For our first example, we make use of the European Randomized Study
of Prostate Cancer Screening (ERSPC) data which ships with the
casebase
package (see
help("ERSPC", package = "casebase")
for details about this
data).
## ScrArm Follow.Up.Time DeadOfPrCa
## 1 Screening group 0.0027 0
## 2 Screening group 0.0027 0
## 3 Screening group 0.0027 0
## 4 Control group 0.0027 0
## 5 Control group 0.0027 0
## 6 Control group 0.0027 0
We first pass this dataset to the popTime
function. If
you do not specify the time
and event
arguments, this function will try to guess them using regular
expressions. See the Details section of
help("popTime", package = "casebase")
for how we try to
guess these inputs.
## 'Follow.Up.Time' will be used as the time variable
We can see its contents and its class:
## ScrArm time event original.time original.event event status
## <fctr> <num> <num> <num> <int> <fctr>
## 1: Screening group 0.0027 0 0.0027 0 censored
## 2: Screening group 0.0027 0 0.0027 0 censored
## 3: Screening group 0.0027 0 0.0027 0 censored
## 4: Control group 0.0027 0 0.0027 0 censored
## 5: Control group 0.0027 0 0.0027 0 censored
## 6: Control group 0.0027 0 0.0027 0 censored
## ycoord yc n_available
## <int> <int> <int>
## 1: 159893 0 0
## 2: 159892 0 0
## 3: 159891 0 0
## 4: 159890 0 0
## 5: 159889 0 0
## 6: 159888 0 0
## [1] "popTime" "data.table" "data.frame"
The casebase
package has a plot
method for
objects of class popTime
:
One benefit from these plots is that it allows you to see the incidence density. This can be seen from the distribution of the red dots in the above plot. We can see that more events are observed later on in time. Therefore a constant hazard model would not be appropriate in this instance as it would overestimate the cumulative incidence earlier on in time, and underestimate it later on.
The unique ‘step shape’ of the population time plot is due to the randomization of the Finnish cohorts which were carried out on January 1 of each of the 4 years 1996 to 1999. This, coupled with the uniform December 31 2006 censoring date, lead to large numbers of men with exactly 11, 10, 9 or 8 years of follow-up.
It is important to note that the red points are random distributed across the gray area for each time of event. That is, imagine we draw a vertical line at a specific event time. We then plot the red point at a randomly sampled y-coordinate along this vertical line. This is done to avoid having all red points along the upper edge of the plot (because the subjects with the least amount of observation time are plotted at the top of the y-axis). By randomly distributing them, we can get a better sense of the incidence density.
Often the observations in a study are in specific groups such as
treatment arms. It may be of interest to compare the population time
plots between these two groups. Here we compare the control group and
the screening group. We create exposure stratified plots by specifying
the exposure
argument in the popTime
function:
# stratified population time plot
pt_object_strat <- casebase::popTime(ERSPC,
event = "DeadOfPrCa",
exposure = "ScrArm")
## 'Follow.Up.Time' will be used as the time variable
We can see its contents, class and that it has an exposure attribute:
## ScrArm time event original.time original.event event status ycoord
## <fctr> <num> <num> <num> <int> <fctr> <int>
## 1: Control group 0.0027 0 0.0027 0 censored 88232
## 2: Control group 0.0027 0 0.0027 0 censored 88231
## 3: Control group 0.0027 0 0.0027 0 censored 88230
## 4: Control group 0.0027 0 0.0027 0 censored 88229
## 5: Control group 0.0137 0 0.0137 0 censored 88228
## 6: Control group 0.0137 0 0.0137 0 censored 88227
## yc n_available
## <int> <int>
## 1: 0 0
## 2: 0 0
## 3: 0 0
## 4: 0 0
## 5: 0 0
## 6: 0 0
## [1] "popTime" "data.table" "data.frame"
We can also see that the pt_object_strat
has an exposure
attribute which contains the name of the exposure variable in the
dataset:
## [1] "ScrArm"
The plot method for objects of class popTime
will use
this exposure attribute to create exposure stratified population time
plots:
We can also plot them side-by-side using the
facet.params
argument, which is a list of arguments that
are passed to the facet_wrap()
function in the ggplot2
package:
To illustrate the casebase sampling methodology, we can also plot the
base series using the add.base.series
function:
Note that the theme.params
argument is a list of
arguments passed to the ggplot2::theme()
function.
Next we show population time plots when there is a competing event.
The bmtcrr
dataset contains information on patients who
underwent haematopoietic stem cell transplantation for acute leukaemia.
This data is included in the casebase
package. See
help("bmtcrr", package = "casebase")
for more details. We
will use this dataset to further illustrate the fundamental components
of a population time plot.
Note that for this dataset, the popTime
fails to
identify a time
variable if you didn’t specify one:
## 'data.frame': 177 obs. of 7 variables:
## $ Sex : Factor w/ 2 levels "F","M": 2 1 2 1 1 2 2 1 2 1 ...
## $ D : Factor w/ 2 levels "ALL","AML": 1 2 1 1 1 1 1 1 1 1 ...
## $ Phase : Factor w/ 4 levels "CR1","CR2","CR3",..: 4 2 3 2 2 4 1 1 1 4 ...
## $ Age : int 48 23 7 26 36 17 7 17 26 8 ...
## $ Status: int 2 1 0 2 2 2 0 2 0 1 ...
## $ Source: Factor w/ 2 levels "BM+PB","PB": 1 1 1 1 1 1 1 1 1 1 ...
## $ ftime : num 0.67 9.5 131.77 24.03 1.47 ...
##
## ALL AML
## 0 17 29
## 1 28 28
## 2 28 47
## Error in checkArgsTimeEvent(data = data, time = time, event = event): data does not contain time variable
In this case, you must be explicit about what the time variable is:
## 'Status' will be used as the event variable
## [1] "popTime" "data.table" "data.frame"
We first plot that area on the graph representing the observed
follow-up time. Fundamentally, this area is constructed by plotting a
line for each individual, where the length of each line represents their
follow-up time in the cohort. The follow-up times are plotted from top
(shortest follow-up time) to bottom (longest follow-up time). In
practice, we instead plot a polygon using the ggplot2::geom_ribbon()
function. The following figure shows this area for the
bmtcrr
dataset. Note that we must specify
add.case.series = FALSE
because the default is to add the
case series:
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's fill values.
Note that we can change the aesthetics of the area by using the
ribbon.params()
argument as follows. These arguments are
passed to the ggplot2::geom_ribbon()
function:
plot(popTimeData,
add.case.series = FALSE,
ribbon.params = list(color = "red", fill = "blue", size = 2, alpha = 0.2))
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's fill values.
Next we add the case series. Note that because the
Status
column has a competing event (coded as 2), we must
specify comprisk = TRUE
(even if you don’t want to plot the
competing event):
In the above plot we can clearly see many of the deaths occur at the beginning, so in this case, a constant hazard assumption isn’t valid. This information is useful when deciding on the type of model to use.
We can now add the base series with the add.base.series
argument. Internally, the plot
method calls the
casebase::sampleCaseBase
function to sample the base series
from the total person moments. This requires us to specify the ratio of
base series to case series in the ratio
argument which we
will leave at its default of 1. A legend is also added by default:
We specify the add.competing.event = TRUE
in order to
also plot the competing event. Note, that like the case series, the
competing event is sampled randomly on the vertical axis in order to see
the incidence density.
plot(popTimeData,
add.case.series = TRUE,
add.base.series = TRUE,
add.competing.event = TRUE,
comprisk = TRUE)
We can also only plot the case series and competing event (or any combination):
Next we stratify by disease; lymphoblastic or myeloblastic leukemia,
abbreviated as ALL and AML, respectively. We must specify the
exposure
variable. Furthermore it is important to properly
label the factor variable corresponding to the exposure variable; this
will ensure proper labeling of the panels:
## 'Status' will be used as the event variable
## [1] "D"
Here is some code to change color points and legend labels. For a more thorough description, please see the Customizing Population Time Plots vignette.
plot(popTimeData,
add.case.series = TRUE,
add.base.series = TRUE,
add.competing.event = TRUE,
comprisk = TRUE,
case.params = list(mapping = aes(x = time, y = yc, colour = "Relapse", fill = "Relapse")),
base.params = list(mapping = aes(x = time, y = ycoord, colour = "Base series", fill = "Base series")),
competing.params = list(mapping = aes(x = time, y = yc, colour = "Competing event", fill = "Competing event")),
fill.params = list(name = "Legend Name",
breaks = c("Relapse", "Base series", "Competing event"),
values = c("Relapse" = "blue",
"Competing event" = "red",
"Base series" = "orange"))
)
## Warning in plot.popTime(popTimeData, add.case.series = TRUE, add.base.series =
## TRUE, : fill.params has been specified by the user but color.params has not.
## Setting color.params to be equal to fill.params.
Below are the steps to create a population time plot for the
Veterans’ Administration Lung Cancer study (see
help("veteran", package = "survival")
for more details on
this dataset).
## Warning in data("veteran"): data set 'veteran' not found
## 'data.frame': 137 obs. of 8 variables:
## $ trt : num 1 1 1 1 1 1 1 1 1 1 ...
## $ celltype: Factor w/ 4 levels "squamous","smallcell",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ time : num 72 411 228 126 118 10 82 110 314 100 ...
## $ status : num 1 1 1 1 1 1 1 1 1 0 ...
## $ karno : num 60 70 60 60 70 20 40 80 50 70 ...
## $ diagtime: num 7 5 3 9 11 5 10 29 18 6 ...
## $ age : num 69 64 38 63 65 49 69 68 43 70 ...
## $ prior : num 0 10 0 10 10 0 10 0 0 0 ...
## 'time' will be used as the time variable
## 'status' will be used as the event variable
## [1] "popTime" "data.table" "data.frame"