Tendril package contains functions designed to compute the x-y coordinates and to build a Tendril plot. Inspired by the notabilia visualization, the Tendril plot was developed to capture the relative effect of different kind of adverse events for two treatments, including temporal aspects, in a single visualization. Specifically, each tendril (branch) in the Tendril plot represents a type of adverse effect, and the direction of the tendril is dictated by on which treatment arm the event is occurring. If an event is occurring on the first of the two specified treatment arms, the tendril bends clockwise (to the right). If an event is occurring on the second of the treatment arms, the tendril bends anti-clockwise (to the left).
library(Tendril) data("TendrilData") test <- Tendril(mydata = TendrilData, rotations = Rotations, AEfreqThreshold = 9, Tag = "Comment", Treatments = c("placebo", "active"), Unique.Subject.Identifier = "subjid", Terms = "ae", Treat = "treatment", StartDay = "day", SubjList = SubjList, SubjList.subject = "subjid", SubjList.treatment = "treatment" ) plot(test)
In the plots above, a clinical trial with two treatment arms, placebo and active, and 80 different adverse effects were simulated (“AE1” to “AE80”). As outlined above, the Tendril plot is based on an algorithm that evaluates each type of adverse event (AE) in sequence, producing a collection of tendrils (branches) that effectively summarizes the time-resolved safety profile of a clinical trial within a single plot. Events on the first treatment (placebo) cause that tendril to bend clockwise to the right, and each event on the second treatment (active) causes the tendril to bend anti-clockwise to the left. The resulting tree-like structure clearly displays those adverse events having the largest differences in relative risk (see AE40); AEs having only a transient increased risk bending and then straightening (see AE42); and AEs that are balanced over the treatment arms (see AE44). In the first plot each tendril is colored according to adverse event type and in the second, each event has been colored according to the false discovery rate adjusted p value. There are a number of statistical measures that could be used for colouring, see the plot.Tendril documentation.
The result of the
Tendril function is an object of the class
Tendril that can be referenced as a base R list. It contains the following elements:
data: a dataframe containing the original data, the calculated angles and coordinates used to produce the tendril plot and the statistical analysis results
Terms: the name of the variable in the source dataset that records the event type (e.g. adverse event)
Treat: the name of the variable in the source dataset that records the treatment
Treatments: the available values of Treatments
StartDay: the name of the variable in the source dataset that records the start day of the adverse event
Unique.Subject.Identifier: the name of the variable in the source dataset that records the subject identifier
AEfreqThreshold: the frequency threshold used to select tendrils
Tag: a text label associated with the analysis
n.tot: a dataframe with a single row and variables for the total number of events recorded for each of the treatments
SubjList: A dataframe listing all the subjects in the trial, including those not having an AE, and corresponding treatments
SubjList.subject: the name of the column in
SubjListcontaining the subject IDs
SubjList.treatment: the name of the column in
SubjListcontaining the treatments names
The result of the
TendrilPerm function is an object of the class
TendrilPerm. This object can also be referenced as a list with the following elements:
Tendrilobject corresponding to the arguments passed to
PermTerm: The event type for which permutations are computed
perm.data: A dataframe recording the coordinates of the permuted tendril data
tendril.pi: An object of class
TendrilPirecording estimated percentiles on the assumption of balance between treatment arms
TendrilPerm function outputs an object which contains an element of class
TendrilPi. This is structurally similar to a data frame, with equal length vector elements for event day (
Tag, number of terms (
label (whether upper or lower limit),
"Percentile") and the day from which to permute (
Tendril function requires several arguments. The key argument is
mydata a data frame with at least four columns, corresponding to a subject identifier, treatment arm, event type and day (relative to randomisation) of onset. Four character variables are also passed to denote the column name of the required columns; these are
StartDay respectively. If any additional columns are present then these are retained for subsequent analysis.
Additionally arguments are provided for the unadjusted angular displacement of each event (
rotations, either a single value for all records or a vector which can vary by row of
mydata); a minimum value for the number of events in at least one arm (
AEfreqThreshold); a text label to apply to the analysis as a whole (
Tag); the two treatments to be compared (
Treatments, any other treatments are ignored).
A data frame can optionally be passed as the argument
SubjList which lists all the subjects in the trial, including those not having an AE, and the corresponding treatments to which each subject has been randomised along with (optionally) the day to which each subject was followed up. Even though the SubjList data frame is optional, it is required to calculate statistics and simulate permuted tendrils (described below). Three character arguments (
SubjList.dropoutday) are then also passed to allow the variables in
SubjList to be correctly identified.
Finally a number of binary flags can be passed to further control the analysis.
compensate_imbalance_groups allows for treatment group imbalance to be compensated for, provided
SubjList is present.
filter_double_events allows either all, or just the first event of each type to be recorded for each subject. Finally,
suppress_warnings allows warnings from the Chi-square test to be disabled, as low counts can result in multiple warning messages.
A typical Tendril dataset might look like this:
## subjid treatment ae day ## 1 ID240 placebo AE40 134 ## 2 ID101 placebo AE43 263 ## 3 ID101 placebo AE41 44 ## 4 ID102 placebo AE37 134 ## 5 ID102 placebo AE36 98 ## 6 ID102 placebo AE39 50
Note the four columns containing the subject IDs (
subjid), the treatment (
treatment), the adverse effect term (
ae) and the days (
Tendril() function could then be called as:
test <- Tendril(mydata = TendrilData, rotations = Rotations, AEfreqThreshold = 9, Tag = "Comment", Treatments = c("placebo", "active"), Unique.Subject.Identifier = "subjid", Terms = "ae", Treat = "treatment", StartDay = "day", SubjList = SubjList, SubjList.subject = "subjid", SubjList.treatment = "treatment" )
NB: If there is any missing data in the subject identifier, treatment, event type or onset day then such rows will be removed.
The function checks that the arguments are valid and then computes the angles and coordinates of the x and y points in the tendrils based on the balance of events between treatments with the angular displacement being determined by the argument
The time between consecutive events of each type is proportional to the distance between connected points on the tendril plot. The angular displacement at each point is determined by the excess number of treatments on the first arm (rotating clockwise) or the excess number of treatments on the second arm (rotating anticlockwise) at each point in time.
If the argument
SubjList provides a data frame of subjects, treatments and optionally drop-out days then
Tendril calls the function
tendril_stat to estimate the statistical significance of the imbalance at each data point. Statistical significance is estimated using an unadjusted Chi-square test (
p), a Chi-square test false discovery rate (FDR) adjusted locally per AE (
p.adj), or Fisher’s Exact test (
fish). Additional statistics are provided for the risk difference (
rdiff), risk ratio (
RR) and odds ratio (
TendrilPerm function required an object of class
Tendril to be passed (as
tendril) which is the basis of permutations of the treatment assignment. An argument is also supplied with the event type (
PermTerm) for which permutations are required. All other event types are ignored in the analysis and removed from the results.
Arguments can also be provided to specify the number of permutations (
n.perm; defaults to 100), the day from which to permute treatments (
perm.from.day; defaults to 1), the lower proportion to estimate (
pi.low; defaults to 0.1, i.e. 10th percentile), and the upper proportion to estimate (
pi.high; defaults to 0.9, i.e. 90th percentile).
As well as calculating permuted tendrils on the basis of randomly permuted treatment assignments (corresponding to a hypothesis of no imbalance between treatment arms) the
TendrilPerm function also returns tendrils corresponding to the specified percentiles of these permutations. These facilitate comparison with the observed tendril to identify any event types with significant imbalance between treatment arms.
The use of the
perm.from.day argument can be useful to explore temporal effects, for example where there is a strong imbalance initially, which subsequently resolves, with balanced incidence of events from a certain point in time onward.
The function outputs a list with four elements including the input tendril data filtered for the selected event type, the event type selected, the permuted tendril details, and the percentile details in the form of a
An example of an invocation of the
TendrilPerm function, using the
Tendril object generated above is as follows.
This function can be invoked as
plot() applied to a
Tendril object. As well as providing a
Tendril object the user can optionally supply
coloring controls how the points on the tendril plot are coloured, and defaults to
Terms meaning that each event type is coloured differently and a legend provided. Alternatively
OR will colour each point on the tendril scale according to the relevant statistic at that specific plot point.
term argument allows the plot to display only specific event types. The default is
NULL which means that all tendrils are displayed. Alternatively, a single value can be supplied, or a vector of multiple terms. In all cases tendrils are only displayed subject to the
The following plots illustrate some sample tendril plots.
These are generated using the
ggplot2 package and so can be amended using features from the
Tendril plots can also be produced in interactive mode using the
plotly package. These are requested by passing the optional argument
interactive=TRUE. These interactive plots allow access to feature such as zooming, and hovering over points to obtain information such as the event type, the FDR p-value and the total number of events of that type.
This function is also invoked as
plot, but applied to a
TendrilPerm object. As well as passing a
TendrilPerm object the user can again specify a
coloring argument. This applies equivalent colouring to that used in
plot.Tendril but applied only to the selected event type, as defined in the call to
TendrilPerm. Permuted tendrils are coloured in light grey.
There is also an optional
percentile=TRUE argument which will overlay the percentiles specified in the call to
TendrilPerm. These are shown as two dark grey lines.
The following plots illustrate example permutation plots.
Again, these plots are produced using
ggplot2 and can be modified accordingly.
This function requires a
Tendril object to be supplied and optionally a
term argument, which defaults to
term argument operates in an equivalent manner to in the
plot.Tendril function, and allows specific event types to be selected, unless
NULL is supplied, in which case all tendrils are displayed.
plot_timeseries function shows the event balance as a linear, rather than radial, plot, with time on the horizontal axis and the event balance on the vertical axis.
Example time series plots are shown below.