diff --git a/R/internal.R b/R/internal.R
index 28fcbe2..e4c714f 100644
--- a/R/internal.R
+++ b/R/internal.R
@@ -204,6 +204,22 @@
     (maxval - minval) + start_continuous_endpoint
 }
 
+.logTicks <- function(range) {
+  a <- floor(log2(range[1]))
+  b <- ceiling(log2(range[2]))
+  steps <- unique(round(pretty(c(a, b))))
+  return((2 ^ steps))
+}
+
+.log10Ticks <- function(range) {
+  range <- log10(range)
+  get_axp <- function(x) 10^c(floor(x[1]), ceiling(x[2]))
+  n <- ifelse(range[2] > 4, 1, 2)
+  steps <- axTicks(side = 1, usr = range, axp = c(get_axp(range), n = n),
+                   log = TRUE)
+  return((steps))
+}
+
 # Computes the continuous information
 .compute_continuous <- function(
     hce_dat, meta, ecdf_mod, step_outcomes, last_outcome, arm_levels) {
@@ -308,9 +324,11 @@
 
   binary_data <- rbind(data.frame("outcome" = last_outcome,
                                   "arm" = actv,
+                                  "value" = 1,
                                   actv_point),
     data.frame("outcome" = last_outcome,
                "arm" = ctrl,
+               "value" = 1,
                ctrl_point)
   )
 
@@ -438,7 +456,8 @@
 .maraca_from_hce_data <- function(x, last_outcome, arm_levels,
                                   fixed_followup_days, compute_win_odds,
                                   step_types = "tte",
-                                  last_type = "continuous") {
+                                  last_type = "continuous",
+                                  lowerBetter = FALSE) {
 
   checkmate::assert_string(last_outcome)
   checkmate::assert_names(names(x),
@@ -478,7 +497,8 @@
     fixed_followup_days = fixed_followup_days,
     compute_win_odds = compute_win_odds,
     step_types = step_types,
-    last_type = last_type
+    last_type = last_type,
+    lowerBetter = lowerBetter
   )
 
   return(maraca_obj)
diff --git a/R/internal_winOdds.R b/R/internal_winOdds.R
index cbc6da8..72279e5 100644
--- a/R/internal_winOdds.R
+++ b/R/internal_winOdds.R
@@ -35,11 +35,22 @@
 
 # Computes the win odds from the internal data.
 .compute_win_odds <- function(hce_dat, arm_levels,
-                              step_outcomes, last_outcome) {
+                              step_outcomes, last_outcome,
+                              lowerBetter) {
 
   `%>%` <- dplyr::`%>%`
 
   hce_dat <- base::as.data.frame(hce_dat)
+  idx_last <- hce_dat$outcome == last_outcome
+
+  # Reversing continous outcome variables if lower is considered better
+  if (lowerBetter) {
+    hce_dat[idx_last, "value"] <-
+      (min(hce_dat[idx_last, "value"], na.rm = TRUE) -
+         hce_dat[idx_last, "value"] +
+         max(hce_dat[idx_last, "value"], na.rm = TRUE))
+  }
+
   hce_dat <- .with_ordered_column(hce_dat)
   fit <- hce::calcWO(x = hce_dat, AVAL = "ordered",
                      TRTP = "arm",
@@ -56,7 +67,7 @@
   endpoints <- c(step_outcomes, last_outcome)
   labs <- c(sapply(head(seq_along(endpoints), -1), function(i) {
     paste(endpoints[1:i], collapse = " +\n")
-  }), "All")
+  }), "Overall")
 
   hce_dat <- hce_dat %>%
     dplyr::mutate_at(dplyr::vars(outcome), factor, levels = c(endpoints, "X"))
@@ -88,13 +99,17 @@
     wo$outcome <- endpoints[i]
     wo$GROUP <- labs[i]
     wo %>%
-      dplyr::rename(dplyr::all_of(c(wins = "WIN", losses = "LOSS",
-                                    ties = "TIE"))) %>%
-      tidyr::pivot_longer(cols = c(wins, losses, ties)) %>%
-      dplyr::mutate_at(dplyr::vars(name), factor,
-                       levels = c("wins", "losses", "ties"))
+      dplyr::rename(dplyr::all_of(c(A_wins = "WIN", P_wins = "LOSS",
+                                    Ties = "TIE"))) %>%
+      tidyr::pivot_longer(cols = c("A_wins", "P_wins", "Ties"),
+                        names_to = "name", values_to = "value")
+    # %>%
+    #   dplyr::mutate_at(dplyr::vars(name), factor,
+    #                    levels = c("wins", "losses", "ties"))
   }))
 
+  wo_bar <- .label_win_odds_plots(wo_bar, arm_levels)
+
   wins_forest$GROUP <- factor(wins_forest$GROUP, levels = rev(labs))
   wins_forest$method <- factor(wins_forest$method,
                                levels = c("win ratio", "win odds"))
@@ -143,21 +158,26 @@
   # Calculate percentage results
   wo_bar_nc$percentage <- 100 * (wo_bar_nc$value / wo_bar_nc$total)
 
+  wo_bar_nc <- .label_win_odds_plots(wo_bar_nc, arms)
+
+  return(wo_bar_nc)
+}
+
+.label_win_odds_plots <- function(bar_data, arms) {
   labels <- c(paste(arms["active"], "wins"),
               paste(arms["control"], "wins"),
               "Ties")
 
-  wo_bar_nc$name <- ifelse(wo_bar_nc$name == "A_wins",
-                           labels[1],
-                           ifelse(wo_bar_nc$name == "P_wins",
-                                  labels[2], labels[3]))
+  bar_data$name <- ifelse(bar_data$name == "A_wins",
+                          labels[1],
+                          ifelse(bar_data$name == "P_wins",
+                                labels[2], labels[3]))
 
-  wo_bar_nc$name <- factor(wo_bar_nc$name, levels = labels)
+  bar_data$name <- factor(bar_data$name, levels = labels)
 
-  return(wo_bar_nc)
+  return(bar_data)
 }
 
-
 # The main plotting function creating the component plot
 .create_component_plot <- function(wo_bar_nc, endpoints, theme) {
 
@@ -193,7 +213,12 @@
 }
 
 # Create forest plot part of cumulative plot
-.create_forest_plot <- function(wins_forest, theme) {
+.create_forest_plot <- function(wins_forest, theme, reverse) {
+
+  if (reverse) {
+    wins_forest$GROUP <- factor(wins_forest$GROUP,
+                                levels = rev(levels(wins_forest$GROUP)))
+  }
 
   plot <- ggplot(data = wins_forest) +
     geom_errorbar(aes(x = GROUP, y = value, ymin = LCL, ymax = UCL,
@@ -228,14 +253,18 @@
 }
 
 # Create bar plot part of cumulative plot
-.create_bar_plot <- function(wo_bar, theme) {
+.create_bar_plot <- function(wo_bar, theme, reverse) {
+
+  if (reverse) {
+    wo_bar$GROUP <- factor(wo_bar$GROUP, levels = rev(levels(wo_bar$GROUP)))
+  }
 
   plot <-  ggplot(data = wo_bar, aes(x = GROUP, y = percentage, fill = name)) +
     geom_bar(stat = "identity", position = position_dodge(), width = .9) +
     coord_flip() + # make bar plot horizontal
     geom_text(aes(label = round(percentage, 1)),
               position = ggplot2::position_dodge(width = .9),
-              vjust = 0.5, hjust = 1.2)
+              vjust = 0.5, hjust = -0.2)
 
   plot <- switch(theme,
                  "maraca" = .theme_maraca_cp(plot),
diff --git a/R/maraca.R b/R/maraca.R
index 5c3423d..3ef0af6 100644
--- a/R/maraca.R
+++ b/R/maraca.R
@@ -36,6 +36,12 @@
 #' @param last_type A single string giving the type of the last outcome.
 #'                  Possible values are "continuous" (default), "binary" or
 #'                  "multinomial".
+#' @param 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.
 #' @param 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
@@ -74,6 +80,7 @@ maraca <- function(
   compute_win_odds = FALSE,
   step_types = "tte",
   last_type = "continuous",
+  lowerBetter = FALSE,
   tte_outcomes = lifecycle::deprecated(),
   continuous_outcome = lifecycle::deprecated()
 ) {
@@ -122,6 +129,8 @@ maraca <- function(
                            choices = c("continuous", "binary"),
                            empty.ok = FALSE)
 
+  checkmate::assert_flag(lowerBetter)
+
   if (!(length(fixed_followup_days) %in%
           c(1, length(step_outcomes[step_types == "tte"])))) {
     stop(paste("fixed_followup_days needs to be either a single value or",
@@ -200,7 +209,8 @@ maraca <- function(
                    "wins_forest" = NULL, "wo_bar" = NULL)
   if (compute_win_odds) {
     win_odds <- .compute_win_odds(hce_dat, arm_levels,
-                                  step_outcomes, last_outcome)
+                                  step_outcomes, last_outcome,
+                                  lowerBetter)
   }
 
   return(
@@ -219,7 +229,8 @@ maraca <- function(
         win_odds = win_odds[["win_odds"]],
         win_odds_outcome = win_odds[["win_odds_outcome"]],
         wins_forest = win_odds[["wins_forest"]],
-        wo_bar = win_odds[["wo_bar"]]
+        wo_bar = win_odds[["wo_bar"]],
+        lowerBetter = lowerBetter
       ),
       class = c("maraca")
     )
@@ -269,8 +280,10 @@ print.maraca <- function(x, ...) {
 #' @param obj an object of S3 class 'maraca'
 #' @param continuous_grid_spacing_x The spacing of the x grid to use for the
 #'        continuous section of the plot.
-#' @param trans the transformation to apply to the data before plotting.
-#'        The accepted values are the same that ggplot2::scale_x_continuous
+#' @param 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".
 #' @param density_plot_type which type of plot to display in the continuous
 #'        part of the plot. Options are "default", "violin", "box", "scatter".
 #' @param vline_type what the vertical dashed line should represent. Accepts
@@ -300,8 +313,8 @@ print.maraca <- function(x, ...) {
 #' @export
 plot_maraca <- function(
     obj, continuous_grid_spacing_x = NULL,
-    trans = "identity",
-    density_plot_type = "default",
+    trans = c("identity", "log", "log10", "sqrt", "reverse")[1],
+    density_plot_type = c("default", "violin", "box", "scatter")[1],
     vline_type = NULL,
     theme = "maraca") {
 
@@ -313,6 +326,10 @@ plot_maraca <- function(
   }
 
   checkmate::assert_string(trans)
+  checkmate::assert_subset(trans,
+                           choices = c("identity", "log", "log10",
+                                       "sqrt", "reverse"),
+                           empty.ok = FALSE)
 
   aes <- ggplot2::aes
   `%>%` <- dplyr::`%>%`
@@ -325,6 +342,11 @@ plot_maraca <- function(
   last_data <- obj$data_last_outcome
   last_type <- obj$last_type
 
+  if (last_type == "binary" && trans %in% c("log", "log10", "sqrt")) {
+    stop(paste(trans, "transformation only implemented for continuous",
+               "last endpoint."))
+  }
+
   vline_type <-
     switch(last_type,
            "continuous" = .checks_continuous_outcome(density_plot_type,
@@ -339,16 +361,15 @@ plot_maraca <- function(
     meta[meta$outcome == obj$last_outcome, ]$startx
 
   if (is.null(continuous_grid_spacing_x)) {
-    continuous_grid_spacing_x <- ifelse(last_type == "continuous", 10, 0.1)
+    continuous_grid_spacing_x <- 10
   }
 
-  plotdata_ecdf <- ecdf_mod$data[, c("outcome", "arm",
+  plotdata_ecdf <- ecdf_mod$data[, c("outcome", "arm", "value",
                                      "adjusted.time", "step_values",
                                      "type")]
-  names(plotdata_ecdf) <- c("outcome", "arm", "x", "y", "type")
-  plotdata_last <- last_data$data[, c("outcome", "arm", "x", "y")]
+  names(plotdata_ecdf) <- c("outcome", "arm", "value", "x", "y", "type")
+  plotdata_last <- last_data$data[, c("outcome", "arm", "value", "x", "y")]
   plotdata_last$type <- last_type
-  names(plotdata_last) <- c("outcome", "arm", "x", "y", "type")
 
   # Add points at (0, 0) on both curves so that they start from the origin
   add_points <- plotdata_ecdf %>%
@@ -399,7 +420,7 @@ plot_maraca <- function(
 
   plotdata_ecdf <- plotdata_ecdf[order(plotdata_ecdf$x), ]
 
-  plotdata <- as.data.frame(rbind(plotdata_ecdf, plotdata_last))
+  plotdata <- rbind(plotdata_ecdf, plotdata_last)
 
   scale <- sign(log10(continuous_grid_spacing_x)) * floor(
     abs(log10(continuous_grid_spacing_x))
@@ -407,18 +428,78 @@ plot_maraca <- function(
 
   if (last_type == "continuous") {
 
-    minor_grid <- .minor_grid(
-      last_data$data$value, scale, continuous_grid_spacing_x
-    )
-
-    range <- c(min(last_data$data$value, na.rm = TRUE),
-               max(last_data$data$value, na.rm = TRUE))
+    range <- c(min(plotdata_last$value, na.rm = TRUE),
+               max(plotdata_last$value, na.rm = TRUE))
+
+    if (trans %in% c("log", "log10", "sqrt")) {
+      minor_grid <- switch(trans,
+                         "log" = .logTicks(range),
+                         "log10" = .log10Ticks(range),
+                         "sqrt" = pretty(range))
+      minor_grid <- minor_grid[minor_grid >= range[1] &
+                                 minor_grid <= range[2]]
+      minor_grid_x <- eval(parse(text = paste0(trans, "(minor_grid)")))
+    } else {
+      minor_grid <- .minor_grid(plotdata_last$value, scale,
+                                continuous_grid_spacing_x)
+      minor_grid_x <- minor_grid
+    }
 
   } else if (last_type == "binary") {
 
-    minor_grid <- seq(0, 100, continuous_grid_spacing_x)
-    range <- c(0, 100)
+    lowest_value <- min(plotdata_last$value, na.rm = TRUE)
+    highest_value <- max(plotdata_last$value, na.rm = TRUE)
+    range <- c(min(0, floor(lowest_value / 10) * 10),
+               max(100, ceiling(highest_value / 10) * 10))
+    minor_grid <- seq(range[1], range[2], continuous_grid_spacing_x)
+    minor_grid_x <- minor_grid
+
+  }
+
+  vline_data <- NULL
+  if (vline_type == "median") {
+    vline_data <- last_data$meta %>%
+      dplyr::select("x" = median, arm)
+  } else if (vline_type == "mean") {
+    vline_data <- last_data$meta %>%
+      dplyr::select("x" = median, arm)
+  }
+
+  if (trans %in% c("log", "log10", "sqrt")) {
 
+    if (range[1] < 0) {
+      warning(paste("Continuous endpoint has negative values - the",
+                    trans, "transformation will result in missing values"))
+    }
+    plotdata_last$value <- eval(parse(text = paste0(trans,
+                                                    "(plotdata_last$value)")))
+    range <- c(min(plotdata_last$value, na.rm = TRUE),
+               max(plotdata_last$value, na.rm = TRUE))
+    plotdata_last$x <- .to_rangeab(plotdata_last$value, start_last_endpoint,
+                                   range[1], range[2])
+
+    if (!is.null(vline_data)) {
+      vline_data$x <- eval(parse(text = paste0(trans, "(vline_data$x)")))
+    }
+  }
+
+  if (trans == "reverse") {
+    if (!is.null(win_odds) && !obj$lowerBetter) {
+      message(paste("Last endpoint axis has been reversed, which might",
+                    "indicate that lower values are considered advantageuos.",
+                    "Note that the win odds were calculated assuming that",
+                    "higher values are better. If that is not correct, please",
+                    "use the parameter lowerBetter = TRUE in the",
+                    "maraca function."))
+    }
+
+    minor_grid_x <- rev(minor_grid_x)
+    minor_grid <- rev(minor_grid)
+    plotdata_last$x <- start_last_endpoint - plotdata_last$x + 100
+
+    if (!is.null(vline_data)) {
+      vline_data$x <- start_last_endpoint - plotdata_last$x + 100
+    }
   }
 
   # Plot the information in the Maraca plot
@@ -428,26 +509,14 @@ plot_maraca <- function(
       color = "grey80"
     )
 
-  if (vline_type == "median") {
-    plot <- plot +
-      ggplot2::geom_vline(
-        mapping = ggplot2::aes(
-          xintercept = median,
-          color = arm
-        ),
-        data = last_data$meta,
-        linetype = "dashed",
-        linewidth = 0.8,
-        show.legend = FALSE
-      )
-  } else if (vline_type == "mean") {
+  if (!is.null(vline_data)) {
     plot <- plot +
       ggplot2::geom_vline(
         mapping = ggplot2::aes(
-          xintercept = average,
+          xintercept = x,
           color = arm
         ),
-        data = last_data$meta,
+        data = vline_data,
         linetype = "dashed",
         linewidth = 0.8,
         show.legend = FALSE
@@ -456,14 +525,15 @@ plot_maraca <- function(
 
   for (outcome in step_outcomes[which_tte]) {
     plot <- plot +
-      ggplot2::geom_step(data = plotdata[plotdata$outcome == outcome, ],
+      ggplot2::geom_step(data =
+                           plotdata_ecdf[plotdata_ecdf$outcome == outcome, ],
                          aes(x = x, y = y, color = arm))
   }
 
   if (length(which_binary) > 0) {
 
-    tmp <- plotdata[plotdata$outcome %in% step_outcomes[which_binary], ]
-
+    tmp <- plotdata_ecdf[plotdata_ecdf$outcome %in%
+                           step_outcomes[which_binary], ]
     tmp <- tmp[order(tmp$x), ]
 
     if (step_types[length(step_types)] == "binary") {
@@ -500,7 +570,7 @@ plot_maraca <- function(
 
   if (step_types[length(step_types)] == "binary") {
 
-    tmp <- plotdata %>%
+    tmp <- plotdata_ecdf %>%
       dplyr::filter(outcome == step_outcomes[length(step_types)]) %>%
       dplyr::group_by(arm) %>%
       dplyr::slice_tail(n = -1) %>%
@@ -543,19 +613,19 @@ plot_maraca <- function(
   } else if (density_plot_type == "violin") {
     plot <- plot +
       ggplot2::geom_violin(
-        data = plotdata[plotdata$type == "continuous", ],
+        data = plotdata_last,
         aes(x = x, y = y, colour = arm, fill = arm), alpha = 0.5
       )
   } else if (density_plot_type == "box") {
     plot <- plot +
       ggplot2::geom_boxplot(
-        data = plotdata[plotdata$type == "continuous", ],
+        data = plotdata_last,
         aes(x = x, y = y, colour = arm, fill = arm), alpha = 0.5
       )
   } else if (density_plot_type == "scatter") {
     plot <- plot +
       ggplot2::geom_jitter(
-        data = plotdata[plotdata$type == "continuous", ],
+        data = plotdata_last,
         aes(x = x, y = y, color = arm),
         # Jittering only vertically, keep the correct x-value
         width = 0
@@ -569,27 +639,28 @@ plot_maraca <- function(
       return(as.character(round(x, -s + 1)))
     }
   )
+
+  m_breaks <- .to_rangeab(
+    minor_grid_x,
+    start_last_endpoint,
+    range[1],
+    range[2]
+    )
+
+  if (trans == "reverse") {
+    m_breaks <- start_last_endpoint - m_breaks + 100
+  }
+
   plot <- plot +
     ggplot2::scale_x_continuous(
       limits = c(0, 100),
       breaks = c(meta$proportion / 2 + meta$startx + 0.1),
       labels = c(obj$step_outcomes, obj$last_outcome),
-      minor_breaks = .to_rangeab(
-        minor_grid,
-        start_last_endpoint,
-        range[1],
-        range[2]
-      ),
-      trans = trans
+      minor_breaks = m_breaks
     ) +
     ggplot2::annotate(
       geom = "text",
-      x = .to_rangeab(
-        minor_grid,
-        start_last_endpoint,
-        range[1],
-        range[2]
-      ),
+      x = m_breaks,
       y = 0,
       label = labels,
       color = "grey60"
@@ -667,7 +738,7 @@ validate_maraca_plot <- function(x,  ...) {
   proportions <- diff(pb$data[[1]][, c("xintercept")])
   names(proportions) <- unique(x$data$outcome)
 
-  arms <- levels(pb$plot$data[, pb$plot$labels$colour])
+  arms <- levels(unlist(pb$plot$data[, pb$plot$labels$colour]))
 
   tte_data <- .create_validation_tte(layers, x, arms)
   binary_step_data <- .create_validation_binary_step(layers, x, arms)
@@ -711,8 +782,10 @@ validate_maraca_plot <- function(x,  ...) {
 #' @param \dots not used
 #' @param continuous_grid_spacing_x The spacing of the x grid to use for the
 #'        continuous section of the plot.
-#' @param trans the transformation to apply to the data before plotting.
-#'        The accepted values are the same that ggplot2::scale_x_continuous
+#' @param 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".
 #' @param density_plot_type The type of plot to use to represent the density.
 #'        Accepts "default", "violin", "box" and "scatter".
 #' @param vline_type what the vertical dashed line should represent. Accepts
@@ -741,8 +814,10 @@ validate_maraca_plot <- function(x,  ...) {
 #'
 #' @export
 plot.maraca <- function(
-    x, continuous_grid_spacing_x = 10, trans = "identity",
-    density_plot_type = "default",
+    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",
     ...) {
@@ -765,8 +840,10 @@ plot.maraca <- function(
 #'                   "active" and "control".
 #' @param continuous_grid_spacing_x The spacing of the x grid to use for the
 #'        continuous section of the plot.
-#' @param trans the transformation to apply to the data before plotting.
-#'        The accepted values are the same that ggplot2::scale_x_continuous
+#' @param 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".
 #' @param density_plot_type The type of plot to use to represent the density.
 #'        Accepts "default", "violin", "box" and "scatter".
 #' @param vline_type what the vertical dashed line should represent. Accepts
@@ -796,6 +873,12 @@ plot.maraca <- function(
 #'        For more details, check the vignette called
 #'        "Maraca Plots - Themes and Styling".
 #'        [companion vignette for package users](themes.html)
+#' @param 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.
 #' @param continuous_outcome Deprecated and substituted by the more general
 #'                           'last_outcome'. A single string containing the
 #'                           continuous outcome label.
@@ -814,14 +897,17 @@ plot.maraca <- function(
 plot.hce <- function(x, last_outcome = "C",
                      arm_levels = c(active = "A", control = "P"),
                      continuous_grid_spacing_x = 10,
-                     trans = "identity",
-                     density_plot_type = "default",
+                     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,
                      continuous_outcome = lifecycle::deprecated(),
                      ...) {
 
@@ -835,7 +921,8 @@ plot.hce <- function(x, last_outcome = "C",
                                       fixed_followup_days,
                                       compute_win_odds,
                                       step_types = step_types,
-                                      last_type = last_type)
+                                      last_type = last_type,
+                                      lowerBetter = lowerBetter)
 
   plot_maraca(maraca_obj, continuous_grid_spacing_x,
               trans, density_plot_type, vline_type, theme)
diff --git a/R/winOddsPlots.R b/R/winOddsPlots.R
index 5cec958..e82a90d 100644
--- a/R/winOddsPlots.R
+++ b/R/winOddsPlots.R
@@ -110,6 +110,12 @@ component_plot.maraca <- function(x,
 #'        Options are "maraca", "color1", "color2" and none".
 #'        For more details, check the vignette called
 #'        "Maraca Plots - Plotting win odds".
+#' @param 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.
 #' @param continuous_outcome Deprecated and substituted by the more general
 #'                           'last_outcome'. A single string containing the
 #'                           continuous outcome label.
@@ -128,13 +134,15 @@ component_plot.hce <- function(x, last_outcome = "C",
                                arm_levels = c(active = "A", control = "P"),
                                fixed_followup_days = NULL,
                                theme = "maraca",
+                               lowerBetter = FALSE,
                                continuous_outcome = lifecycle::deprecated(),
                                ...) {
 
   # Create maraca object
   maraca_dat <- .maraca_from_hce_data(x, last_outcome, arm_levels,
                                       fixed_followup_days,
-                                      compute_win_odds = TRUE)
+                                      compute_win_odds = TRUE,
+                                      lowerBetter = lowerBetter)
 
   # Get win odds by outcome from maraca object
   win_odds_outcome <- maraca_dat$win_odds_outcome
@@ -182,11 +190,14 @@ cumulative_plot.default <- function(x, ...) {
 #' Check the vignette "Maraca Plots - Plotting win odds" for more details.
 #'
 #' @param x an object of S3 class 'maraca'.
-#' @param \dots not used
 #' @param 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".
+#' @param reverse Flag indicating if the cumulated outcomes should be
+#'        displayed in order from top to bottom (FALSE, the default)
+#'        or in reverse (TRUE).
+#' @param \dots not used
 #' @return Cumulative plot as a patchwork object.
 #' @examples
 #'
@@ -208,7 +219,8 @@ cumulative_plot.default <- function(x, ...) {
 #' cumulative_plot(maraca_dat)
 #'
 #' @export
-cumulative_plot.maraca <- function(x, theme = "maraca", ...) {
+cumulative_plot.maraca <- function(x, theme = "maraca",
+                                   reverse = FALSE, ...) {
 
   # Check that win odds were calculated for the maraca object
   if (is.null(x[["wins_forest"]]) || is.null(x[["wo_bar"]])) {
@@ -221,8 +233,8 @@ cumulative_plot.maraca <- function(x, theme = "maraca", ...) {
   wo_bar <- x$wo_bar
   wins_forest <- x$wins_forest
   # Create forest plot
-  plot_bar <- .create_bar_plot(wo_bar, theme)
-  plot_forest <- .create_forest_plot(wins_forest, theme)
+  plot_bar <- .create_bar_plot(wo_bar, theme, reverse)
+  plot_forest <- .create_forest_plot(wins_forest, theme, reverse)
 
   plot <-  patchwork:::"|.ggplot"(plot_bar, plot_forest) +
     patchwork::plot_layout(widths = c(2.5, 1), nrow = 1)
@@ -261,6 +273,15 @@ cumulative_plot.maraca <- function(x, theme = "maraca", ...) {
 #'        Options are "maraca", "color1", "color2" and none".
 #'        For more details, check the vignette called
 #'        "Maraca Plots - Plotting win odds".
+#' @param reverse Flag indicating if the cumulated outcomes should be
+#'        displayed in order from top to bottom (FALSE, the default)
+#'        or in reverse (TRUE).
+#' @param 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.
 #' @param continuous_outcome Deprecated and substituted by the more general
 #'                           'last_outcome'. A single string containing the
 #'                           continuous outcome label.
@@ -279,15 +300,18 @@ cumulative_plot.hce <- function(x, last_outcome = "C",
                                 arm_levels = c(active = "A", control = "P"),
                                 fixed_followup_days = NULL,
                                 theme = "maraca",
+                                reverse = FALSE,
+                                lowerBetter = FALSE,
                                 continuous_outcome = lifecycle::deprecated(),
                                 ...) {
 
   # Create maraca object
   maraca_dat <- .maraca_from_hce_data(x, last_outcome, arm_levels,
                                       fixed_followup_days,
-                                      compute_win_odds = TRUE)
+                                      compute_win_odds = TRUE,
+                                      lowerBetter = lowerBetter)
 
-  plot <- cumulative_plot(maraca_dat, theme = theme)
+  plot <- cumulative_plot(maraca_dat, theme = theme, reverse = reverse)
 
   return(plot)
 }
diff --git a/man/component_plot.hce.Rd b/man/component_plot.hce.Rd
index 43008fd..8b676fc 100644
--- a/man/component_plot.hce.Rd
+++ b/man/component_plot.hce.Rd
@@ -13,6 +13,7 @@ Check the vignette "Maraca Plots - Plotting win odds" for more details.}
   arm_levels = c(active = "A", control = "P"),
   fixed_followup_days = NULL,
   theme = "maraca",
+  lowerBetter = FALSE,
   continuous_outcome = lifecycle::deprecated(),
   ...
 )
@@ -45,6 +46,13 @@ Options are "maraca", "color1", "color2" and none".
 For more details, check the vignette called
 "Maraca Plots - Plotting win odds".}
 
+\item{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.}
+
 \item{continuous_outcome}{Deprecated and substituted by the more general
 'last_outcome'. A single string containing the
 continuous outcome label.}
diff --git a/man/cumulative_plot.hce.Rd b/man/cumulative_plot.hce.Rd
index 82f27e7..13973b1 100644
--- a/man/cumulative_plot.hce.Rd
+++ b/man/cumulative_plot.hce.Rd
@@ -13,6 +13,8 @@ Check the vignette "Maraca Plots - Plotting win odds" for more details.}
   arm_levels = c(active = "A", control = "P"),
   fixed_followup_days = NULL,
   theme = "maraca",
+  reverse = FALSE,
+  lowerBetter = FALSE,
   continuous_outcome = lifecycle::deprecated(),
   ...
 )
@@ -45,6 +47,17 @@ Options are "maraca", "color1", "color2" and none".
 For more details, check the vignette called
 "Maraca Plots - Plotting win odds".}
 
+\item{reverse}{Flag indicating if the cumulated outcomes should be
+displayed in order from top to bottom (FALSE, the default)
+or in reverse (TRUE).}
+
+\item{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.}
+
 \item{continuous_outcome}{Deprecated and substituted by the more general
 'last_outcome'. A single string containing the
 continuous outcome label.}
diff --git a/man/cumulative_plot.maraca.Rd b/man/cumulative_plot.maraca.Rd
index f400843..a268459 100644
--- a/man/cumulative_plot.maraca.Rd
+++ b/man/cumulative_plot.maraca.Rd
@@ -9,7 +9,7 @@ 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.}
 \usage{
-\method{cumulative_plot}{maraca}(x, theme = "maraca", ...)
+\method{cumulative_plot}{maraca}(x, theme = "maraca", reverse = FALSE, ...)
 }
 \arguments{
 \item{x}{an object of S3 class 'maraca'.}
@@ -19,6 +19,10 @@ Options are "maraca", "color1", "color2" and none".
 For more details, check the vignette called
 "Maraca Plots - Plotting win odds".}
 
+\item{reverse}{Flag indicating if the cumulated outcomes should be
+displayed in order from top to bottom (FALSE, the default)
+or in reverse (TRUE).}
+
 \item{\dots}{not used}
 }
 \value{
diff --git a/man/maraca.Rd b/man/maraca.Rd
index 20e698d..78e73e6 100644
--- a/man/maraca.Rd
+++ b/man/maraca.Rd
@@ -17,6 +17,7 @@ maraca(
   compute_win_odds = FALSE,
   step_types = "tte",
   last_type = "continuous",
+  lowerBetter = FALSE,
   tte_outcomes = lifecycle::deprecated(),
   continuous_outcome = lifecycle::deprecated()
 )
@@ -68,6 +69,13 @@ in the vector are "tte" (default) or "binary".}
 Possible values are "continuous" (default), "binary" or
 "multinomial".}
 
+\item{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.}
+
 \item{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
diff --git a/man/plot.hce.Rd b/man/plot.hce.Rd
index 6564d63..ab0f862 100644
--- a/man/plot.hce.Rd
+++ b/man/plot.hce.Rd
@@ -9,14 +9,15 @@
   last_outcome = "C",
   arm_levels = c(active = "A", control = "P"),
   continuous_grid_spacing_x = 10,
-  trans = "identity",
-  density_plot_type = "default",
+  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,
   continuous_outcome = lifecycle::deprecated(),
   ...
 )
@@ -38,8 +39,10 @@ be specified if you have labels different from
 \item{continuous_grid_spacing_x}{The spacing of the x grid to use for the
 continuous section of the plot.}
 
-\item{trans}{the transformation to apply to the data before plotting.
-The accepted values are the same that ggplot2::scale_x_continuous}
+\item{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".}
 
 \item{density_plot_type}{The type of plot to use to represent the density.
 Accepts "default", "violin", "box" and "scatter".}
@@ -76,6 +79,13 @@ For more details, check the vignette called
 "Maraca Plots - Themes and Styling".
 [companion vignette for package users](themes.html)}
 
+\item{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.}
+
 \item{continuous_outcome}{Deprecated and substituted by the more general
 'last_outcome'. A single string containing the
 continuous outcome label.}
diff --git a/man/plot.maraca.Rd b/man/plot.maraca.Rd
index a05c92d..3e26618 100644
--- a/man/plot.maraca.Rd
+++ b/man/plot.maraca.Rd
@@ -7,8 +7,8 @@
 \method{plot}{maraca}(
   x,
   continuous_grid_spacing_x = 10,
-  trans = "identity",
-  density_plot_type = "default",
+  trans = c("identity", "log", "log10", "sqrt", "reverse")[1],
+  density_plot_type = c("default", "violin", "box", "scatter")[1],
   vline_type = NULL,
   theme = "maraca",
   ...
@@ -20,8 +20,10 @@
 \item{continuous_grid_spacing_x}{The spacing of the x grid to use for the
 continuous section of the plot.}
 
-\item{trans}{the transformation to apply to the data before plotting.
-The accepted values are the same that ggplot2::scale_x_continuous}
+\item{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".}
 
 \item{density_plot_type}{The type of plot to use to represent the density.
 Accepts "default", "violin", "box" and "scatter".}
diff --git a/man/plot_maraca.Rd b/man/plot_maraca.Rd
index d48954c..207c820 100644
--- a/man/plot_maraca.Rd
+++ b/man/plot_maraca.Rd
@@ -7,8 +7,8 @@
 plot_maraca(
   obj,
   continuous_grid_spacing_x = NULL,
-  trans = "identity",
-  density_plot_type = "default",
+  trans = c("identity", "log", "log10", "sqrt", "reverse")[1],
+  density_plot_type = c("default", "violin", "box", "scatter")[1],
   vline_type = NULL,
   theme = "maraca"
 )
@@ -19,8 +19,10 @@ plot_maraca(
 \item{continuous_grid_spacing_x}{The spacing of the x grid to use for the
 continuous section of the plot.}
 
-\item{trans}{the transformation to apply to the data before plotting.
-The accepted values are the same that ggplot2::scale_x_continuous}
+\item{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".}
 
 \item{density_plot_type}{which type of plot to display in the continuous
 part of the plot. Options are "default", "violin", "box", "scatter".}
diff --git a/tests/testthat/test_maraca.R b/tests/testthat/test_maraca.R
index b6900d0..c1a118d 100644
--- a/tests/testthat/test_maraca.R
+++ b/tests/testthat/test_maraca.R
@@ -517,7 +517,8 @@ test_that("winOddsData", {
   )
 
   win_odds_list <- .compute_win_odds(data, arm_levels,
-                                     step_outcomes, last_outcome)
+                                     step_outcomes, last_outcome,
+                                     lowerBetter = FALSE)
   win_odds <- win_odds_list[["win_odds"]]
 
   expect_equal(class(win_odds), "numeric")
@@ -1095,8 +1096,11 @@ test_that("gridSpacing", {
 test_that("scaleTransform", {
   file <- fixture_path("hce_scenario_c.csv")
   args <- .maraca_args(file)
+  dat <- args$data
+  dat[dat$GROUP == "Continuous outcome", "AVAL0"] <-
+    dat[dat$GROUP == "Continuous outcome", "AVAL0"] + 50
   mar <- maraca(
-    args$data,
+    dat,
     args$step_outcomes,
     args$last_outcome,
     args$arm_levels,