Update author list with IDs

Author: Monika-H

DESCRIPTION

@@ -4,10 +4,13 @@ Type: Package
 Title: The Maraca Plot: Visualization of Hierarchical Composite Endpoints in Clinical Trials
 License: Apache License (>= 2)
 Authors@R: c(
-    person("Martin Karpefors", "", email = "martin.karpefors@astrazeneca.com", role = "aut"),
-    person("Samvel B. Gasparyan", "", email = "samvel.gasparyan@astrazeneca.com", role = "aut"),
-    person("Monika Huhn", "", email = "monika.huhn@astrazeneca.com", role = c("aut", "cre")),
-    person("Stefano Borini", "", email = "stefano.borini@astrazeneca.com", role = c("ctb")))
+    person("Martin Karpefors", "", email = "martin.karpefors@astrazeneca.com", role = "aut",
+	   comment = c(ORCID = "0000-0003-3136-9882")),
+    person("Samvel B. Gasparyan", "", email = "samvel.gasparyan@astrazeneca.com", role = "aut",
+	   comment = c(ORCID = "0000-0002-4797-2208")),
+    person("Stefano Borini", "", email = "stefano.borini@astrazeneca.com", role = c("ctb")),
+    person("Monika Huhn", "", email = "monika.huhn@astrazeneca.com", role = c("aut", "cre"),
+	   comment = c(ORCID = "0009-0000-7865-6008")))
 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,

vignettes/winOdds.Rmd

@@ -1,173 +1,176 @@
----
-title: "Maraca Plots - Plotting win odds"
-author: "Monika Huhn"
-date: "10/10/2023"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Maraca Plots - Plotting win odds}
-  %\VignetteEngine{knitr::rmarkdown}
-  \usepackage[utf8]{inputenc}
----
-
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(echo = TRUE, collapse = TRUE)
-library(dplyr)
-library(ggplot2)
-library(maraca)
-```
-
-## Component plot
-
-The maraca package also contains an additional plot
-called `"component_plot"`. This one allows to plot the
-different components that make up the win odds calculation.
-More specifically, for each outcome, the plot shows how
-often patients in each treatment arm "won" against the other
-arm. For the time-to-event endpoints, this means counting how many
-patients of the other arm had no more prioritized event prior.
-For the continuous outcome this means counting how many patients had a lower value.
-The results are separated for each outcome (non-cumulative)
-and also include ties (patients from 2 treatment arms having same
-outcome at the same time/same continuous outcome value).
-
-Let us first read in some data.
-```{r maraca1, eval = TRUE}
-library(maraca)
-
-data(hce_scenario_a)
-```
-
-In order to use the `component_plot`, we have to first create a
-`maraca` object. Important here is to set the argument
-`compute_win_odds = TRUE`, so that the necessary calculations
-are included.
-```{r}
-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"),
-  # Make sure to calculate the win odds
-  compute_win_odds = TRUE
-)
-```
-
-Now we can just plot the object using the `component_plot()` function.
-```{r fig.width=7, fig.height=6}
-component_plot(maraca_dat)
-```
-
-It is also possible to use the `component_plot()` function directly on
-an `hce` object (created using the
-[hce package](https://cran.r-project.org/package=hce)).
-
-```{r fig.width=7, fig.height=6}
-library(hce)
-
-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 <- 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)
-```
-
-## Cumulative plot
-
-Furthermore, there is also a plot called `"cumulative_plot"`.
-Similar to the `component_plot`, this plot shows the different HCE components that
-make up the win odds calculation. Different to the component plot,
-this plot provides insight into the contributed effect for each of the components as
-they are added in sequence (from top to bottom).
-Additionally, there is also a right-hand panel that shows a forest plot with the win odds
-and win ratio corresponding to the same cumulative sequence. To understand the contribution
-from each outcome, we artificially set all the less prioritized outcomes as ties and calculate
-the win odds/ratio. Thus, for each added outcome there will be less ties.  
-
-As before, in order to use the `cumulative_plot`, we have to first create a
-`maraca` object. Important here is to set the argument
-`compute_win_odds = TRUE`, so that the necessary calculations
-are included.
-```{r}
-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"),
-  # Make sure to calculate the win odds
-  compute_win_odds = TRUE
-)
-```
-
-Now we can just plot the object using the `cumulative_plot()` function.
-```{r fig.width=7, fig.height=6}
-cumulative_plot(maraca_dat)
-```
-
-It is also possible to use the `cumulative_plot()` function directly on
-an `hce` object (created using the
-[hce package](https://cran.r-project.org/package=hce)).
-
-```{r fig.width=7, fig.height=6}
-cumulative_plot(hce_dat)
-```
-
-The user can also choose to only display one of the statistics (win odds or win ratio)
-by specifying so in the `include` parameter.
-```{r fig.width=7, fig.height=6}
-cumulative_plot(maraca_dat, include = "win odds")
-```
-
-The y-axis can easily be reversed using the `reverse` parameter.
-```{r fig.width=7, fig.height=6}
-cumulative_plot(hce_dat, reverse = TRUE)
-```
-
-## Styling
-
-The resulting plot for the `component_plot()` functions
-is a normal ggplot2 object that can be styled accordingly.
-```{r fig.width=7, fig.height=6}
-component_plot(maraca_dat) +
-  ggplot2::scale_fill_manual(values = c("seagreen", "red", "grey"), name = NULL)
-```
-
-Note that the `cumulative_plot()` function is using the
-patchwork package to combine 2 ggplot2 objects. They
-can be accessed as list items and styled accordingly.
-```{r fig.width=7, fig.height=6}
-p <- cumulative_plot(maraca_dat)
-p[[1]] <- p[[1]] +
-  ggplot2::scale_fill_manual(values = c("seagreen", "red", "grey"), name = NULL)
-p
-```
-
-For the users convenience, there are also different themes
-available to style the plot.
-
-The default style is called `theme = "maraca"`.
-```{r fig.width=7, fig.height=6}
-component_plot(maraca_dat, theme = "maraca")
-```
-
-There are 2 different themes with different color
-schemes, `theme = "color1"` and `theme = "color2"`.
-```{r fig.width=7, fig.height=6}
-cumulative_plot(maraca_dat, theme = "color1")
-```
-
-```{r fig.width=7, fig.height=6}
-component_plot(maraca_dat, theme = "color2")
-```
-
-There is also a theme without any styling `theme = "none"` that
-can be used as a base when the user wants to style the plot themselves.
-```{r fig.width=8, fig.height=6}
-cumulative_plot(maraca_dat, theme = "none")
-```
+---
+title: "Maraca Plots - Plotting win odds"
+author: "Monika Huhn"
+date: "10/10/2023"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Maraca Plots - Plotting win odds}
+  %\VignetteEngine{knitr::rmarkdown}
+  \usepackage[utf8]{inputenc}
+---
+
+```{r setup, include = FALSE}
+knitr::opts_chunk$set(echo = TRUE, collapse = TRUE)
+library(dplyr)
+library(ggplot2)
+library(maraca)
+```
+
+## Component plot
+
+The maraca package also contains an additional plot
+called `"component_plot"`. This one allows to plot the
+different components that make up the win odds calculation.
+More specifically, for each outcome, the plot shows how
+often patients in each treatment arm "won" against the other
+arm. For the time-to-event endpoints, this means counting how many
+patients of the other arm had no more prioritized event prior.
+For the continuous outcome this means counting how many patients had a lower value.
+The results are separated for each outcome (non-cumulative)
+and also include ties (patients from 2 treatment arms having same
+outcome at the same time/same continuous outcome value).
+
+Let us first read in some data.
+```{r maraca1, eval = TRUE}
+library(maraca)
+
+data(hce_scenario_a)
+```
+
+In order to use the `component_plot`, we have to first create a
+`maraca` object. Important here is to set the argument
+`compute_win_odds = TRUE`, so that the necessary calculations
+are included.
+```{r}
+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"),
+  # Make sure to calculate the win odds
+  compute_win_odds = TRUE
+)
+```
+
+Now we can just plot the object using the `component_plot()` function.
+```{r fig.width=7, fig.height=6}
+component_plot(maraca_dat)
+```
+
+It is also possible to use the `component_plot()` function directly on
+an `hce` object (created using the
+[hce package](https://cran.r-project.org/package=hce)).
+
+```{r fig.width=7, fig.height=6}
+library(hce)
+
+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 <- 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)
+```
+
+## Cumulative plot
+
+Furthermore, there is a plot called `"cumulative_plot"`.
+Similar to the `component_plot`, this plot shows the different HCE components that
+make up the win odds calculation. Different to the component plot,
+this plot provides insight into the contributed effect for each of the components as
+they are added in sequence (from top to bottom).
+Additionally, there is also a right-hand panel that shows a forest plot with the win odds
+and win ratio corresponding to the same cumulative sequence. To understand the contribution
+from each outcome, we artificially set all the less prioritized outcomes as ties and calculate
+the win odds/ratio. Thus, for each added outcome there will be less ties.  
+
+As before, in order to use the `cumulative_plot`, we have to first create a
+`maraca` object. Important here is to set the argument
+`compute_win_odds = TRUE`, so that the necessary calculations
+are included.
+```{r}
+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"),
+  # Make sure to calculate the win odds
+  compute_win_odds = TRUE
+)
+```
+
+Now we can just plot the object using the `cumulative_plot()` function.
+```{r fig.width=7, fig.height=6}
+cumulative_plot(maraca_dat)
+```
+
+It is also possible to use the `cumulative_plot()` function directly on
+an `hce` object (created using the
+[hce package](https://cran.r-project.org/package=hce)).
+
+```{r fig.width=7, fig.height=6}
+cumulative_plot(hce_dat)
+```
+
+The user can also choose to only display one of the statistics (win odds or win ratio)
+by specifying so in the `include` parameter.
+```{r fig.width=7, fig.height=6}
+cumulative_plot(maraca_dat, include = "win odds")
+```
+
+The y-axis can easily be reversed using the `reverse` parameter.
+```{r fig.width=7, fig.height=6}
+cumulative_plot(hce_dat, reverse = TRUE)
+```
+
+## Styling
+
+The resulting plot for the `component_plot()` functions
+is a normal ggplot2 object that can be styled accordingly.
+```{r fig.width=7, fig.height=6}
+component_plot(maraca_dat) +
+  ggplot2::scale_fill_manual(values = c("seagreen", "red", "grey"), name = NULL)
+```
+
+Note that the `cumulative_plot()` function is using the
+patchwork package to combine 2 ggplot2 objects - the
+bar plot and the forest plot that together make up the
+`cumulative_plot()`. They
+can be accessed as list items and styled accordingly.
+```{r fig.width=7, fig.height=6}
+p <- cumulative_plot(maraca_dat)
+# Accessing the first ggplot2 object and adding styling (bar plot)
+p[[1]] <- p[[1]] +
+  ggplot2::scale_fill_manual(values = c("seagreen", "red", "grey"), name = NULL)
+p
+```
+
+For the users convenience, there are also different themes
+available to style the plot.
+
+The default style is called `theme = "maraca"`.
+```{r fig.width=7, fig.height=6}
+component_plot(maraca_dat, theme = "maraca")
+```
+
+There are 2 different themes with different color
+schemes, `theme = "color1"` and `theme = "color2"`.
+```{r fig.width=7, fig.height=6}
+cumulative_plot(maraca_dat, theme = "color1")
+```
+
+```{r fig.width=7, fig.height=6}
+component_plot(maraca_dat, theme = "color2")
+```
+
+There is also a theme without any styling `theme = "none"` that
+can be used as a base when the user wants to style the plot themselves.
+```{r fig.width=8, fig.height=6}
+cumulative_plot(maraca_dat, theme = "none")
+```