In AstraZeneca/Tendril: Compute and Display Tendril Plots
Introduction
The 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)
plot(test, coloring = "p.adj")
plot(test, term = c("AE40", "AE42", "AE44"))
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.
Objects within the Tendril package
The Tendril class
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 resultsTerms : 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 treatmentTreatments : the available values of TreatmentsStartDay : the name of the variable in the source dataset that records the start day of the adverse eventUnique.Subject.Identifier : the name of the variable in the source dataset that records the subject identifierAEfreqThreshold : the frequency threshold used to select tendrilsTag : a text label associated with the analysisn.tot : a dataframe with a single row and variables for the total number of events recorded for each of the treatmentsSubjList : A dataframe listing all the subjects in the trial, including those not having an AE, and corresponding treatmentsSubjList.subject : the name of the column in SubjList containing the subject IDsSubjList.treatment : the name of the column in SubjList containing the treatments names
The TendrilPerm class
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:
tendril : A Tendril object corresponding to the arguments passed to TendrilPermPermTerm : The event type for which permutations are computedperm.data : A dataframe recording the coordinates of the permuted tendril datatendril.pi : An object of class TendrilPi recording estimated percentiles on the assumption of balance between treatment arms
The TendrilPi class
The 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 (StartDay), Terms, x and y coordinates,
Tag, number of terms (TermsCount), label (whether upper or lower limit),
type ("Percentile") and the day from which to permute (perm.from.day).
Functions within the Tendril package
Tendril()
The 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 Unique.Subject.Identifier, Treat, Terms
and 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.subject, SubjList.treatment and
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:
head(TendrilData)
Note the four columns containing the subject IDs (subjid), the treatment
(treatment), the adverse effect term (ae) and the days (day).
The 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 rotations.
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 (OR).
TendrilPerm
The 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 TendrilPi object.
An example of an invocation of the TendrilPerm function, using the Tendril object generated above is as follows.
perm <- TendrilPerm(test,
"AE40",
n.perm = 500)
perm2 <- TendrilPerm(test,
"AE42",
n.perm = 500,
perm.from.day = 120)
Plot functions
plot.Tendril
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
and term arguments. 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 p, p.adj, FDR.tot, fish,
rdiff, RR and OR will colour each point on the tendril scale according to
the relevant statistic at that specific plot point.
The 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 AEfreqThreshold argument.
The following plots illustrate some sample tendril plots.
plot(test)
plot(test, coloring = "p.adj")
plot(test, term = "AE40", coloring = "fish")
plot(test, term = paste("AE", c(40, 42, 44), sep = ""), coloring = "Terms")
These are generated using the ggplot2 package and so can be amended using features from the ggplot2 package.
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.
plot.TendrilPerm
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.
plot(perm)
plot(perm, coloring = "OR", percentile = TRUE)
plot(perm2, coloring = "p.adj", percentile = TRUE)
Again, these plots are produced using ggplot2 and can be modified accordingly.
plot_timeseries
This function requires a Tendril object to be supplied and optionally a term argument, which defaults to NULL. The 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.
The 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.
plot_timeseries(test)
plot_timeseries(test, term = paste("AE", c(40, 42, 44), sep = ""))
Complete usage example
The following code will use the provided sample data TendrilData and SubjList to produce a complete analysis: compute tendril data, compute statistics, compute permutations for one of the adverse effects and produce a plot:
#load library
library("Tendril")
#compute tendril data
data.tendril <- 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",
filter_double_events = FALSE,
suppress_warnings = FALSE)
#compute permutations
data.tendril <- TendrilPerm(tendril = data.tendril,
PermTerm="AE40",
n.perm = 200,
perm.from.day = 1)
#do plot
p <- plot(data.tendril$tendril)
#plot permutations
p <- plot(data.tendril)
#plot permutations and percentile
p <- plot(data.tendril, percentile = TRUE)
#save tendril coordinates and stat results
write.table(data.tendril$tendril$data, "mydata.txt", sep="\t", row.names = FALSE)
#save permutation coordinates
write.table(data.tendril$perm.data, "my_permutation_data.txt", sep="\t", row.names = FALSE)
#save permutation percentiles
write.table(data.tendril$tendril.pi, "my_percentile_data.txt", sep="\t", row.names = FALSE)
References
Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary of the incidence, significance and temporal aspects of adverse events in clinical trials" - JAMIA 2018; 25(8): 1069-1073
AstraZeneca/Tendril documentation built on May 27, 2022, 10:07 p.m.
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Introduction
The 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) plot(test, coloring = "p.adj") plot(test, term = c("AE40", "AE42", "AE44"))
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.
Objects within the Tendril package
The Tendril class
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 resultsTerms: 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 treatmentTreatments: the available values of TreatmentsStartDay: the name of the variable in the source dataset that records the start day of the adverse eventUnique.Subject.Identifier: the name of the variable in the source dataset that records the subject identifierAEfreqThreshold: the frequency threshold used to select tendrilsTag: a text label associated with the analysisn.tot: a dataframe with a single row and variables for the total number of events recorded for each of the treatmentsSubjList: A dataframe listing all the subjects in the trial, including those not having an AE, and corresponding treatmentsSubjList.subject: the name of the column inSubjListcontaining the subject IDsSubjList.treatment: the name of the column inSubjListcontaining the treatments names
The TendrilPerm class
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:
tendril: ATendrilobject corresponding to the arguments passed toTendrilPermPermTerm: The event type for which permutations are computedperm.data: A dataframe recording the coordinates of the permuted tendril datatendril.pi: An object of classTendrilPirecording estimated percentiles on the assumption of balance between treatment arms
The TendrilPi class
The 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 (StartDay), Terms, x and y coordinates,
Tag, number of terms (TermsCount), label (whether upper or lower limit),
type ("Percentile") and the day from which to permute (perm.from.day).
Functions within the Tendril package
Tendril()
The 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 Unique.Subject.Identifier, Treat, Terms
and 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.subject, SubjList.treatment and
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:
head(TendrilData)
Note the four columns containing the subject IDs (subjid), the treatment
(treatment), the adverse effect term (ae) and the days (day).
The 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 rotations.
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 (OR).
TendrilPerm
The 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 TendrilPi object.
An example of an invocation of the TendrilPerm function, using the Tendril object generated above is as follows.
perm <- TendrilPerm(test, "AE40", n.perm = 500) perm2 <- TendrilPerm(test, "AE42", n.perm = 500, perm.from.day = 120)
Plot functions
plot.Tendril
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
and term arguments. 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 p, p.adj, FDR.tot, fish,
rdiff, RR and OR will colour each point on the tendril scale according to
the relevant statistic at that specific plot point.
The 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 AEfreqThreshold argument.
The following plots illustrate some sample tendril plots.
plot(test) plot(test, coloring = "p.adj") plot(test, term = "AE40", coloring = "fish") plot(test, term = paste("AE", c(40, 42, 44), sep = ""), coloring = "Terms")
These are generated using the ggplot2 package and so can be amended using features from the ggplot2 package.
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.
plot.TendrilPerm
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.
plot(perm) plot(perm, coloring = "OR", percentile = TRUE) plot(perm2, coloring = "p.adj", percentile = TRUE)
Again, these plots are produced using ggplot2 and can be modified accordingly.
plot_timeseries
This function requires a Tendril object to be supplied and optionally a term argument, which defaults to NULL. The 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.
The 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.
plot_timeseries(test) plot_timeseries(test, term = paste("AE", c(40, 42, 44), sep = ""))
Complete usage example
The following code will use the provided sample data TendrilData and SubjList to produce a complete analysis: compute tendril data, compute statistics, compute permutations for one of the adverse effects and produce a plot:
#load library library("Tendril") #compute tendril data data.tendril <- 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", filter_double_events = FALSE, suppress_warnings = FALSE) #compute permutations data.tendril <- TendrilPerm(tendril = data.tendril, PermTerm="AE40", n.perm = 200, perm.from.day = 1) #do plot p <- plot(data.tendril$tendril) #plot permutations p <- plot(data.tendril) #plot permutations and percentile p <- plot(data.tendril, percentile = TRUE) #save tendril coordinates and stat results write.table(data.tendril$tendril$data, "mydata.txt", sep="\t", row.names = FALSE) #save permutation coordinates write.table(data.tendril$perm.data, "my_permutation_data.txt", sep="\t", row.names = FALSE) #save permutation percentiles write.table(data.tendril$tendril.pi, "my_percentile_data.txt", sep="\t", row.names = FALSE)
References
Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary of the incidence, significance and temporal aspects of adverse events in clinical trials" - JAMIA 2018; 25(8): 1069-1073
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