,
Samvel B. Gasparyan [aut] ,
Stefano Borini [ctb],
Monika Huhn [aut, cre] | Title: | The Maraca Plot: Visualization of Hierarchical Composite Endpoints in Clinical Trials |
|---|---|
| Description: | Library that supports visual interpretation of hierarchical composite endpoints (HCEs). HCEs are complex constructs used as primary endpoints in clinical trials, combining outcomes of different types into ordinal endpoints, in which each patient contributes the most clinically important event (one and only one) to the analysis. See Karpefors M et al. (2022) <doi:10.1177/17407745221134949>. |
| Authors: | Martin Karpefors [aut] ,
Samvel B. Gasparyan [aut] ,
Stefano Borini [ctb],
Monika Huhn [aut, cre] |
| Maintainer: | Monika Huhn <[email protected]> |
| License: | Apache License (>= 2) |
| Version: | 0.7 |
| Built: | 2024-11-13 04:45:04 UTC |
| Source: | https://github.com/astrazeneca/maraca |
Implemented for objects of type 'maraca' and 'hce'.
component_plot(x, ...)component_plot(x, ...)
x |
an object of S3 class 'maraca' or 'hce'. |
... |
further arguments to be passed to the object-specific functions |
Generic function to create a plot showing the components used in calculating win odds (wins and ties) separately for each outcome directly from an hce object. Check the vignette "Maraca Plots - Plotting win odds" for more details.
## S3 method for class 'hce' component_plot( x, step_outcomes = NULL, last_outcome = "C", arm_levels = c(active = "A", control = "P"), fixed_followup_days = NULL, theme = "maraca", lowerBetter = FALSE, ... )## S3 method for class 'hce' component_plot( x, step_outcomes = NULL, last_outcome = "C", arm_levels = c(active = "A", control = "P"), fixed_followup_days = NULL, theme = "maraca", lowerBetter = FALSE, ... )
x |
an object of S3 class 'hce'. |
step_outcomes |
A vector of strings containing the outcome labels for all outcomes displayed as part of the step function on the left side of the plot. The order is kept for the plot. By default (when set to NULL) this is automatically updated by taking the non-continuous outcomes from the GROUP variable in alphabetical order. |
last_outcome |
A single string containing the last outcome label displayed on the right side of the plot. Default value "C". |
arm_levels |
A named vector of exactly two strings, mapping the values used for the active and control arms to the values used in the data. The names must be "active" and "control" in this order. Note that this parameter only need to be specified if you have labels different from "active" and "control". |
fixed_followup_days |
Not needed if HCE object contains information on fixed follow-up days in the study (column PADY or TTEfixed, depending on hce version). Otherwise, this argument must be specified. Note: If argument is specified and HCE object contains PADY or TTEfixed column, then fixed_followup_days argument is used. |
theme |
Choose theme to style the plot. The default theme is "maraca". Options are "maraca", "color1", "color2" and none". For more details, check the vignette called "Maraca Plots - Plotting win odds". |
lowerBetter |
Flag for the final outcome variable, indicating if lower values are considered better/advantageous. This flag is need to make sure the win odds are calculated correctly. Default value is FALSE, meaning higher values are considered advantageous. |
... |
not used |
Component plot as a ggplot2 object.
Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1) Rates_P <- c(2.47, 2.24, 2.9, 4, 6) hce_dat <- hce::simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, CM_A = -3, CM_P = -6, CSD_A = 16, CSD_P = 15, fixedfy = 3, seed = 31337) component_plot(hce_dat)Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1) Rates_P <- c(2.47, 2.24, 2.9, 4, 6) hce_dat <- hce::simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, CM_A = -3, CM_P = -6, CSD_A = 16, CSD_P = 15, fixedfy = 3, seed = 31337) component_plot(hce_dat)
Generic function to create a plot showing the components used in calculating win odds (wins and ties) separately for each outcome directly from a maraca object. Note that for this plot, when creating the maraca object using the maraca() function, the argument "compute_win_odds" has to be set to TRUE. Check the vignette "Maraca Plots - Plotting win odds" for more details.
## S3 method for class 'maraca' component_plot(x, theme = "maraca", ...)## S3 method for class 'maraca' component_plot(x, theme = "maraca", ...)
x |
an object of S3 class 'maraca'. |
theme |
Choose theme to style the plot. The default theme is "maraca". Options are "maraca", "color1", "color2" and none". For more details, check the vignette called "Maraca Plots - Plotting win odds". |
... |
not used |
Component plot as a ggplot2 object.
data(hce_scenario_a) maraca_dat <- maraca(data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) component_plot(maraca_dat)data(hce_scenario_a) maraca_dat <- maraca(data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) component_plot(maraca_dat)
Implemented for objects of type 'maraca' and 'hce'.
cumulative_plot(x, ...)cumulative_plot(x, ...)
x |
an object of S3 class 'maraca' or 'hce'. |
... |
further arguments to be passed to the object-specific functions |
Generic function to create a plot showing the components used in calculating win odds (wins and ties) cumulated for all outcomes directly from an hce object. Check the vignette "Maraca Plots - Plotting win odds" for more details.
## S3 method for class 'hce' cumulative_plot( x, step_outcomes = NULL, last_outcome = "C", arm_levels = c(active = "A", control = "P"), fixed_followup_days = NULL, theme = "maraca", include = c("win odds", "win ratio"), reverse = FALSE, lowerBetter = FALSE, ... )## S3 method for class 'hce' cumulative_plot( x, step_outcomes = NULL, last_outcome = "C", arm_levels = c(active = "A", control = "P"), fixed_followup_days = NULL, theme = "maraca", include = c("win odds", "win ratio"), reverse = FALSE, lowerBetter = FALSE, ... )
x |
an object of S3 class 'hce'. |
step_outcomes |
A vector of strings containing the outcome labels for all outcomes displayed as part of the step function on the left side of the plot. The order is kept for the plot. By default (when set to NULL) this is automatically updated by taking the non-continuous outcomes from the GROUP variable in alphabetical order. |
last_outcome |
A single string containing the last outcome label displayed on the right side of the plot. Default value "C". |
arm_levels |
A named vector of exactly two strings, mapping the values used for the active and control arms to the values used in the data. The names must be "active" and "control" in this order. Note that this parameter only need to be specified if you have labels different from "active" and "control". |
fixed_followup_days |
Not needed if HCE object contains information on fixed follow-up days in the study (column PADY or TTEfixed, depending on hce version). Otherwise, this argument must be specified. Note: If argument is specified and HCE object contains PADY or TTEfixed column, then fixed_followup_days argument is used. |
theme |
Choose theme to style the plot. The default theme is "maraca". Options are "maraca", "color1", "color2" and none". For more details, check the vignette called "Maraca Plots - Plotting win odds". |
include |
Vector or single string indicating which statistics to include in the right hand side plot. Acceptable values are "win odds" and/or "win ratio". Default is c("win odds", "win ratio"). |
reverse |
Flag indicating if the cumulated outcomes should be displayed in order from top to bottom (FALSE, the default) or in reverse (TRUE). |
lowerBetter |
Flag for the final outcome variable, indicating if lower values are considered better/advantageous. This flag is need to make sure the win odds are calculated correctly. Default value is FALSE, meaning higher values are considered advantageous. |
... |
not used |
Cumulative plot as a patchwork list. Individual plots can be accessed like list items (plot[[1]] and plot[[2]]).
Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1) Rates_P <- c(2.47, 2.24, 2.9, 4, 6) hce_dat <- hce::simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, CM_A = -3, CM_P = -6, CSD_A = 16, CSD_P = 15, fixedfy = 3, seed = 31337) cumulative_plot(hce_dat)Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1) Rates_P <- c(2.47, 2.24, 2.9, 4, 6) hce_dat <- hce::simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, CM_A = -3, CM_P = -6, CSD_A = 16, CSD_P = 15, fixedfy = 3, seed = 31337) cumulative_plot(hce_dat)
Generic function to create a plot showing the components used in calculating win odds (wins and ties) cumulated for all outcomes directly from a maraca object. Note that for this plot, when creating the maraca object using the maraca() function, the argument "compute_win_odds" has to be set to TRUE. Check the vignette "Maraca Plots - Plotting win odds" for more details.
## S3 method for class 'maraca' cumulative_plot( x, theme = "maraca", include = c("win odds", "win ratio"), reverse = FALSE, ... )## S3 method for class 'maraca' cumulative_plot( x, theme = "maraca", include = c("win odds", "win ratio"), reverse = FALSE, ... )
x |
an object of S3 class 'maraca'. |
theme |
Choose theme to style the plot. The default theme is "maraca". Options are "maraca", "color1", "color2" and none". For more details, check the vignette called "Maraca Plots - Plotting win odds". |
include |
Vector or single string indicating which statistics to include in the right hand side plot. Acceptable values are "win odds" and/or "win ratio". Default is c("win odds", "win ratio"). |
reverse |
Flag indicating if the cumulated outcomes should be displayed in order from top to bottom (FALSE, the default) or in reverse (TRUE). |
... |
not used |
Cumulative plot as a patchwork list. Individual plots can be accessed like list items (plot[[1]] and plot[[2]]).
data(hce_scenario_a) maraca_dat <- maraca(data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) cumulative_plot(maraca_dat)data(hce_scenario_a) maraca_dat <- maraca(data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) cumulative_plot(maraca_dat)
This is example data frame containing the example for scenario A.
data(hce_scenario_a)data(hce_scenario_a)
A data frame with 1000 rows.
The patient identifier
Which type of outcome the row belongs to
Not required for computation. The group as an arbitrary numerical value
Contains both the time-to-event data for hard outcomes and the continuous data for the continuous outcome
Not required for computation. Create an ordered sequence of values where the AVAL0 value associated with the patient is offset by GROUPN
Treatment group
This is example data frame containing the example for scenario B.
data(hce_scenario_b)data(hce_scenario_b)
A data frame with 1000 rows.
The patient identifier
Which type of outcome the row belongs to
Not required for computation. The group as an arbitrary numerical value
Contains both the time-to-event data for hard outcomes and the continuous data for the continuous outcome
Not required for computation. Create an ordered sequence of values where the AVAL0 value associated with the patient is offset by GROUPN
Treatment group
This is example data frame containing the example for scenario C.
data(hce_scenario_c)data(hce_scenario_c)
A data frame with 1000 rows.
The patient identifier
Which type of outcome the row belongs to
Not required for computation. The group as an arbitrary numerical value
Contains both the time-to-event data for hard outcomes and the continuous data for the continuous outcome
Not required for computation. Create an ordered sequence of values where the AVAL0 value associated with the patient is offset by GROUPN
Treatment group
This is example data frame containing the example for scenario D.
data(hce_scenario_d)data(hce_scenario_d)
A data frame with 1000 rows.
The patient identifier
Which type of outcome the row belongs to
Not required for computation. The group as an arbitrary numerical value
Contains both the time-to-event data for hard outcomes and the continuous data for the continuous outcome
Not required for computation. Create an ordered sequence of values where the AVAL0 value associated with the patient is offset by GROUPN
Treatment group
This is example data frame containing the example for scenario KCCQ3.
data(hce_scenario_kccq3)data(hce_scenario_kccq3)
A data frame with 5000 rows.
The patient identifier
Which type of outcome the row belongs to
Not required for computation. The group as an arbitrary numerical value
Contains both the time-to-event data for hard outcomes and the continuous data for the continuous outcome
Not required for computation. Create an ordered sequence of values where the AVAL0 value associated with the patient is offset by GROUPN
Treatment group
Not needed
Not needed
Creates the maraca analysis object as an S3 object of class 'maraca'.
maraca( data, step_outcomes, last_outcome, arm_levels = c(active = "active", control = "control"), column_names = c(outcome = "outcome", arm = "arm", value = "value"), fixed_followup_days = NULL, compute_win_odds = FALSE, step_types = "tte", last_type = "continuous", lowerBetter = FALSE, tte_outcomes = lifecycle::deprecated(), continuous_outcome = lifecycle::deprecated() ) ## S3 method for class 'maraca' print(x, ...)maraca( data, step_outcomes, last_outcome, arm_levels = c(active = "active", control = "control"), column_names = c(outcome = "outcome", arm = "arm", value = "value"), fixed_followup_days = NULL, compute_win_odds = FALSE, step_types = "tte", last_type = "continuous", lowerBetter = FALSE, tte_outcomes = lifecycle::deprecated(), continuous_outcome = lifecycle::deprecated() ) ## S3 method for class 'maraca' print(x, ...)
data |
A data frame with columns for the following information: - outcome column, containing the time-to-event and continuous labels - arm column, containing the arm a given row belongs to. - value column, containing the values. |
step_outcomes |
A vector of strings containing the outcome labels for all outcomes displayed as part of the step function on the left side of the plot. The order is kept for the plot. |
last_outcome |
A single string containing the last outcome label displayed on the right side of the plot. |
arm_levels |
A named vector of exactly two strings, mapping the values used for the active and control arms to the values used in the data. The names must be "active" and "control" in this order. Note that this parameter only need to be specified if you have labels different from "active" and "control". |
column_names |
A named vector to map the outcome, arm, value to the associated column names in the data. The vector names must match in order "outcome", "arm", and "value". Note that this parameter only need to be specified if you have column names different from the ones above. |
fixed_followup_days |
A mandatory specification of the fixed follow-up days in the study. Can be a single integer value for all tte-outcomes or a vector with one integer value per tte-outcome. |
compute_win_odds |
If TRUE compute the win odds, otherwise (default) don't compute them. |
step_types |
The type of each outcome in the step_outcomes vector. Can be a single string (if all outcomes of same type) or a vector of same length as step_outcomes. Possible values in the vector are "tte" (default) or "binary". |
last_type |
A single string giving the type of the last outcome. Possible values are "continuous" (default), "binary" or "multinomial". |
lowerBetter |
Flag for the final outcome variable, indicating if lower values are considered better/advantageous. This flag is need to make sure the win odds are calculated correctly. Default value is FALSE, meaning higher values are considered advantageous. |
tte_outcomes |
Deprecated and substituted by the more general 'step_outcomes'. A vector of strings containing the time-to-event outcome labels. The order is kept for the plot. |
continuous_outcome |
Deprecated and substituted by the more general 'last_outcome'. A single string containing the continuous outcome label. |
x |
an object of class maraca |
... |
further arguments passed to or from other methods. |
An object of class 'maraca'. The object information must be considered private.
data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE )data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE )
Creates and returns the plot of the maraca data.
plot_maraca( obj, continuous_grid_spacing_x = NULL, trans = c("identity", "log", "log10", "sqrt", "reverse")[1], density_plot_type = c("default", "violin", "box", "scatter")[1], vline_type = NULL, theme = "maraca" )plot_maraca( obj, continuous_grid_spacing_x = NULL, trans = c("identity", "log", "log10", "sqrt", "reverse")[1], density_plot_type = c("default", "violin", "box", "scatter")[1], vline_type = NULL, theme = "maraca" )
obj |
an object of S3 class 'maraca' |
continuous_grid_spacing_x |
The spacing of the x grid to use for the continuous section of the plot. |
trans |
the transformation to apply to the x-axis scale for the last outcome. Possible values are "identity", "log" (only for continuous endpoint), "log10" (only for continuous endpoint), "sqrt" (only for continuous endpoint) and "reverse". The default value is "identity". |
density_plot_type |
which type of plot to display in the continuous part of the plot. Options are "default", "violin", "box", "scatter". |
vline_type |
what the vertical dashed line should represent. Accepts "median" (only for continuous last endpoint), "mean", "none" and NULL (default). By default (vline_type = NULL), vline_type will be set to "median" for a continuous last endpoint and to "mean" for a binary last endpoint. |
theme |
Choose theme to style the plot. The default theme is "maraca". Options are "maraca", "maraca_old", "color1", "color2" and none". For more details, check the vignette called "Maraca Plots - Themes and Styling". |
a ggplot2 object of the data. This function will not render the plot immediately. You have to print() the returned object for it to be displayed.
data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) plot <- plot_maraca(hce_test)data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) plot <- plot_maraca(hce_test)
Generic function to plot the hce object using plot().
## S3 method for class 'hce' plot( x, step_outcomes = NULL, last_outcome = "C", arm_levels = c(active = "A", control = "P"), continuous_grid_spacing_x = 10, trans = c("identity", "log", "log10", "sqrt", "reverse")[1], density_plot_type = c("default", "violin", "box", "scatter")[1], vline_type = NULL, fixed_followup_days = NULL, compute_win_odds = FALSE, step_types = "tte", last_type = "continuous", theme = "maraca", lowerBetter = FALSE, tte_outcomes = lifecycle::deprecated(), continuous_outcome = lifecycle::deprecated(), ... )## S3 method for class 'hce' plot( x, step_outcomes = NULL, last_outcome = "C", arm_levels = c(active = "A", control = "P"), continuous_grid_spacing_x = 10, trans = c("identity", "log", "log10", "sqrt", "reverse")[1], density_plot_type = c("default", "violin", "box", "scatter")[1], vline_type = NULL, fixed_followup_days = NULL, compute_win_odds = FALSE, step_types = "tte", last_type = "continuous", theme = "maraca", lowerBetter = FALSE, tte_outcomes = lifecycle::deprecated(), continuous_outcome = lifecycle::deprecated(), ... )
x |
an object of S3 class 'hce'. |
step_outcomes |
A vector of strings containing the outcome labels for all outcomes displayed as part of the step function on the left side of the plot. The order is kept for the plot. By default (when set to NULL) this is automatically updated by taking the non-continuous outcomes from the GROUP variable in alphabetical order. |
last_outcome |
A single string containing the last outcome label displayed on the right side of the plot. Default value "C". |
arm_levels |
A named vector of exactly two strings, mapping the values used for the active and control arms to the values used in the data. The names must be "active" and "control" in this order. Note that this parameter only need to be specified if you have labels different from "active" and "control". |
continuous_grid_spacing_x |
The spacing of the x grid to use for the continuous section of the plot. |
trans |
the transformation to apply to the x-axis scale for the last outcome. Possible values are "identity", "log" (only for continuous endpoint), "log10" (only for continuous endpoint), "sqrt" (only for continuous endpoint) and "reverse". The default value is "identity". |
density_plot_type |
The type of plot to use to represent the density. Accepts "default", "violin", "box" and "scatter". |
vline_type |
what the vertical dashed line should represent. Accepts "median" (only for continuous last endpoint), "mean", "none" and NULL (default). By default (vline_type = NULL), vline_type will be set to "median" for a continuous last endpoint and to "mean" for a binary last endpoint. |
fixed_followup_days |
Not needed if HCE object contains information on fixed follow-up days in the study (column PADY or TTEfixed, depending on hce version). Otherwise, this argument must be specified to give the fixed follow-up days in the study. Can be a single integer value for all tte-outcomes or a vector with one integer value per tte-outcome. Note: If argument is specified and HCE object also contains PADY or TTEfixed column, then fixed_followup_days argument is used. |
compute_win_odds |
If TRUE compute the win odds, otherwise (default) don't compute them. |
step_types |
The type of each outcome in the step_outcomes vector. Can be a single string (if all outcomes of same type) or a vector of same length as step_outcomes. Possible values in the vector are "tte" (default) or "binary". |
last_type |
A single string giving the type of the last outcome. Possible values are "continuous" (default), "binary" or "multinomial". |
theme |
Choose theme to style the plot. The default theme is "maraca". Options are "maraca", "maraca_old", "color1", "color2" and none". For more details, check the vignette called "Maraca Plots - Themes and Styling". [companion vignette for package users](themes.html) |
lowerBetter |
Flag for the final outcome variable, indicating if lower values are considered better/advantageous. This flag is need to make sure the win odds are calculated correctly. Default value is FALSE, meaning higher values are considered advantageous. |
tte_outcomes |
Deprecated and substituted by the more general 'step_outcomes'. A vector of strings containing the time-to-event outcome labels. The order is kept for the plot. |
continuous_outcome |
Deprecated and substituted by the more general 'last_outcome'. A single string containing the continuous outcome label. |
... |
not used |
Returns ggplot2 plot of the hce object.
Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1) Rates_P <- c(2.47, 2.24, 2.9, 4, 6) hce_dat <- hce::simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, CM_A = -3, CM_P = -6, CSD_A = 16, CSD_P = 15, fixedfy = 3, seed = 31337) plot(hce_dat) plot(hce_dat, fixed_followup_days = 3 * 365)Rates_A <- c(1.72, 1.74, 0.58, 1.5, 1) Rates_P <- c(2.47, 2.24, 2.9, 4, 6) hce_dat <- hce::simHCE(n = 2500, TTE_A = Rates_A, TTE_P = Rates_P, CM_A = -3, CM_P = -6, CSD_A = 16, CSD_P = 15, fixedfy = 3, seed = 31337) plot(hce_dat) plot(hce_dat, fixed_followup_days = 3 * 365)
Generic function to plot the maraca object using plot().
## S3 method for class 'maraca' plot( x, continuous_grid_spacing_x = 10, trans = c("identity", "log", "log10", "sqrt", "reverse")[1], density_plot_type = c("default", "violin", "box", "scatter")[1], vline_type = NULL, theme = "maraca", ... )## S3 method for class 'maraca' plot( x, continuous_grid_spacing_x = 10, trans = c("identity", "log", "log10", "sqrt", "reverse")[1], density_plot_type = c("default", "violin", "box", "scatter")[1], vline_type = NULL, theme = "maraca", ... )
x |
An object of S3 class 'maraca'. |
continuous_grid_spacing_x |
The spacing of the x grid to use for the continuous section of the plot. |
trans |
the transformation to apply to the x-axis scale for the last outcome. Possible values are "identity", "log" (only for continuous endpoint), "log10" (only for continuous endpoint), "sqrt" (only for continuous endpoint) and "reverse". The default value is "identity". |
density_plot_type |
The type of plot to use to represent the density. Accepts "default", "violin", "box" and "scatter". |
vline_type |
what the vertical dashed line should represent. Accepts "median" (only for continuous last endpoint), "mean", "none" and NULL (default). By default (vline_type = NULL), vline_type will be set to "median" for a continuous last endpoint and to "mean" for a binary last endpoint. |
theme |
Choose theme to style the plot. The default theme is "maraca". Options are "maraca", "maraca_old", "color1", "color2" and none". For more details, check the vignette called "Maraca Plots - Themes and Styling". |
... |
not used |
Returns ggplot2 plot of the maraca object.
data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) plot(hce_test)data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) plot(hce_test)
This will produce the 4 validation datasets.
validate_maraca_plot(x, ...)validate_maraca_plot(x, ...)
x |
An object of S3 class 'maracaPlot'. |
... |
Not used. |
Creates a list of datasets for validation purposes.
data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) p <- plot(hce_test) validate_maraca_plot(p)data(hce_scenario_a) hce_test <- maraca( data = hce_scenario_a, step_outcomes = c("Outcome I", "Outcome II", "Outcome III", "Outcome IV"), last_outcome = "Continuous outcome", fixed_followup_days = 3 * 365, column_names = c(outcome = "GROUP", arm = "TRTP", value = "AVAL0"), arm_levels = c(active = "Active", control = "Control"), compute_win_odds = TRUE ) p <- plot(hce_test) validate_maraca_plot(p)