@@ -2,6 +2,10 @@
 
 * `tbl_null_report()` now centers its message, so with no body content it reads as a "no data" panel spanning the table instead of text hugging the left edge. (#305)
 
+* `annotate_lineplot_df()` gains `"se"` and `"ci"` as `summary_stats` options, reporting the standard error and the confidence interval of the mean (at a new `conf_level` argument) in the summary table below the plot. (#307)
+
+* `gg_lineplot()` gains a `show_n` argument to append group sizes to the legend (e.g. `"Placebo (N=42)"`) and a `jitter` argument to horizontally separate overlapping groups while keeping points, lines, and error bars aligned. (#307)
+
 * Added `modify_split_caption()` to subtitle each page of a split `{gtsummary}` table (e.g. from `tbl_listing()`, `tbl_baseline_chg()`, or `tbl_shift()`) from its split level via a glue `pattern` (default `"Parameter: {spl_level}"`) and hide the now-redundant split column. (#282)
 
 * `annotate_lineplot_df()` and `annotate_pkc_df()` gain a `font_size` argument to control the summary table font size, matching `annotate_riskdf()`. The `text_size` argument of `annotate_pkc_df()` is soft-deprecated in favor of `font_size`. (#295)
 
@@ -13,7 +13,11 @@
 #'   Optional column names as strings. If `NULL` (default), the function
 #'   automatically extracts these from the `gg_plt` mapping.
 #' @param summary_stats (`character`)\cr
-#'   Vector of statistics to include. Defaults to `c("n", "mean", "sd")`.
+#'   Vector of statistics to include. One or more of `"n"`, `"mean"`, `"sd"`,
+#'   `"se"`, `"ci"`, `"median"`, and `"iqr"`. Defaults to `c("n", "mean", "sd")`.
+#'   `"ci"` reports the confidence interval of the mean at `conf_level`.
+#' @param conf_level (`numeric`)\cr
+#'   Confidence level for the `"ci"` statistic. Defaults to `0.95`.
 #' @param digits (`numeric`, `list`, or `formula`)\cr
 #'   Optional specification for the number of decimal places for the summary statistics.
 #'   Can be a single integer (e.g., `2`), a vector of integers matching the statistics
@@ -63,6 +67,7 @@ annotate_lineplot_df <- function(gg_plt,
                                  y = NULL,
                                  group = NULL,
                                  summary_stats = c("n", "mean", "sd"),
+                                 conf_level = 0.95,
                                  digits = NULL,
                                  font_size = 10,
                                  rel_height_plot = 0.75) {
@@ -72,6 +77,17 @@ annotate_lineplot_df <- function(gg_plt,
     ))
   }
 
+  # Confidence interval bounds for the table, delegated to the same helper that
+  # draws the error bars in `gg_lineplot()` so the table can never disagree with
+  # the plotted interval. They are picked up by name when referenced in the
+  # `{conf.low}` / `{conf.high}` glue of the `statistic` argument below.
+  conf.low <- function(x) {
+    .calc_stats(x, stat = "mean", variability = "ci", conf_level = conf_level)$ymin
+  }
+  conf.high <- function(x) {
+    .calc_stats(x, stat = "mean", variability = "ci", conf_level = conf_level)$ymax
+  }
+
   # Auto-extract variable names from the ggplot mapping if not explicitly provided
   if (is.null(x)) x <- gg_varname_extraction(gg_plt$mapping$x)
   if (is.null(y)) y <- gg_varname_extraction(gg_plt$mapping$y)
@@ -90,14 +106,19 @@ annotate_lineplot_df <- function(gg_plt,
     "n" = "{N_nonmiss}",
     "mean" = "{mean}",
     "sd" = "{sd}",
+    "se" = "{se}",
+    "ci" = "{conf.low}, {conf.high}",
     "median" = "{median}",
     "iqr" = "{p25}, {p75}"
   )
 
+  ci_label <- paste0(round(conf_level * 100), "% CI")
   stat_labels <- c(
     "n" = "n",
     "mean" = "Mean",
     "sd" = "SD",
+    "se" = "SE",
+    "ci" = ci_label,
     "median" = "Median",
     "iqr" = "IQR"
   )
@@ -151,7 +172,7 @@ annotate_lineplot_df <- function(gg_plt,
     dplyr::select(-remove)
 
   raw_labels <- as.character(formatted_df[[1]])
-  is_stat <- trimws(raw_labels) %in% c("n", "Mean", "SD", "Median", "IQR")
+  is_stat <- trimws(raw_labels) %in% trimws(gts_stat_labels)
 
   # Apply Plotmath styling (bold for headers, indent for stats)
   y_labels <- parse(text = ifelse(
 
@@ -19,6 +19,13 @@
 #'   Default is `"ci"`.
 #' @param conf_level (`numeric`)\cr
 #'   Confidence level for error bars when `variability = "ci"` (default: `0.95`).
+#' @param show_n (`flag`)\cr
+#'   When `TRUE`, append the group size to each legend key (e.g.
+#'   `"Placebo (N=42)"`). Only applies when `group` is supplied. Default `FALSE`.
+#' @param jitter (`numeric`)\cr
+#'   Horizontal jitter width applied to points, lines and error bars together
+#'   so they stay aligned. `0` (default) disables jittering and uses dodging
+#'   only. A small value such as `0.1` helps separate overlapping groups.
 #'
 #' @return A `ggplot` object of class `crane_gg_line`.
 #'
@@ -66,17 +73,34 @@
 #'     linetype = "dashed",
 #'     color = "gray50"
 #'   )
+#'
+#' # 4. Show group sizes in the legend and jitter overlapping groups
+#' gg_lineplot(
+#'   data = mock_adlb,
+#'   x = AVISIT,
+#'   y = AVAL,
+#'   group = ARM,
+#'   show_n = TRUE,
+#'   jitter = 0.1
+#' )
 #' @export
 gg_lineplot <- function(data,
                         x,
                         y,
                         group = NULL,
                         stat = c("mean", "median"),
                         variability = c("ci", "sd", "se", "iqr", "none"),
-                        conf_level = 0.95) {
+                        conf_level = 0.95,
+                        show_n = FALSE,
+                        jitter = 0) {
   # 1. Argument Matching and Validation
   stat <- match.arg(stat)
   variability <- match.arg(variability)
+  check_scalar_logical(show_n)
+  check_scalar(jitter)
+  if (!is.numeric(jitter) || jitter < 0) {
+    cli::cli_abort("{.arg jitter} must be a non-negative number.")
+  }
 
   # Prevent mathematically invalid combinations
   if (stat == "mean" && variability == "iqr") {
@@ -137,7 +161,19 @@ gg_lineplot <- function(data,
       dplyr::left_join(data, by = x)
   }
 
-  pd <- ggplot2::position_dodge(width = 0.4)
+  # A single shared position object keeps the points, connecting lines and
+  # error bars aligned. When `jitter > 0` a fixed seed makes every layer apply
+  # the same horizontal jitter, so they never drift apart.
+  pd <- if (jitter > 0) {
+    ggplot2::position_jitterdodge(
+      jitter.width = jitter,
+      jitter.height = 0,
+      dodge.width = 0.4,
+      seed = 123
+    )
+  } else {
+    ggplot2::position_dodge(width = 0.4)
+  }
 
   # 3. Build Plot: Use clean aes() mapping so gg_varname_extraction succeeds
   if (length(group) > 0) {
@@ -201,6 +237,23 @@ gg_lineplot <- function(data,
   # Dynamically label the legends to overwrite the ugly ".data[[group]]" default
   if (length(group) > 0) {
     p <- p + ggplot2::labs(color = group, linetype = group, shape = group)
+
+    # Optionally append the group size to each legend key, e.g. "Placebo (N=42)"
+    if (show_n) {
+      grp_n <- table(data[[group]])
+      grp_labels <- stats::setNames(
+        paste0(names(grp_n), " (N=", as.integer(grp_n), ")"),
+        names(grp_n)
+      )
+      p <- p +
+        ggplot2::scale_color_discrete(labels = grp_labels) +
+        ggplot2::scale_shape_discrete(labels = grp_labels) +
+        ggplot2::scale_linetype_discrete(labels = grp_labels)
+    }
+  } else if (show_n) {
+    cli::cli_warn(
+      "{.arg show_n} has no effect when {.arg group} is not supplied."
+    )
   }
 
 
 
@@ -136,6 +136,87 @@ test_that("annotate_lineplot_df formats numeric digits correctly", {
   expect_true(grepl("\\.[0-9]{2}$", val_sd))
 })
 
+test_that("annotate_lineplot_df computes se and matches sd / sqrt(n)", {
+  mock_df2gg <- function(df, ...) df
+
+  res_df <- testthat::with_mocked_bindings(
+    {
+      annotate_lineplot_df(
+        gg_plt = p_valid,
+        data = mock_adlb,
+        summary_stats = c("n", "mean", "se"),
+        digits = c(0, 4, 4)
+      )
+    },
+    df2gg_aligned = mock_df2gg
+  )
+
+  expect_s3_class(res_df, "data.frame")
+
+  # SE row is present and labelled
+  expect_true(any(grepl("SE$", trimws(res_df$Group))))
+
+  # Verify Visit '0' SE equals sd / sqrt(n) for the first treatment group
+  x <- mock_adlb$AVAL[mock_adlb$AVISIT == 0 & mock_adlb$ARM == "Trt A"]
+  expected_se <- stats::sd(x) / sqrt(length(x))
+  val_se <- trimws(res_df[grepl("SE$", trimws(res_df$Group)), "0"][1])
+
+  expect_equal(as.numeric(val_se), expected_se, tolerance = 1e-3)
+})
+
+test_that("annotate_lineplot_df computes ci and matches a t-based interval", {
+  mock_df2gg <- function(df, ...) df
+
+  res_df <- testthat::with_mocked_bindings(
+    {
+      annotate_lineplot_df(
+        gg_plt = p_valid,
+        data = mock_adlb,
+        summary_stats = c("n", "mean", "ci"),
+        conf_level = 0.95
+      )
+    },
+    df2gg_aligned = mock_df2gg
+  )
+
+  expect_s3_class(res_df, "data.frame")
+
+  # The CI row is labelled with the confidence level
+  expect_true(any(grepl("95% CI", trimws(res_df$Group))))
+
+  # Verify Visit '0' CI equals the t interval for the first treatment group
+  x <- mock_adlb$AVAL[mock_adlb$AVISIT == 0 & mock_adlb$ARM == "Trt A"]
+  n <- length(x)
+  err <- stats::qt(0.975, df = n - 1) * stats::sd(x) / sqrt(n)
+  expected <- c(mean(x) - err, mean(x) + err)
+
+  val_ci <- res_df[grepl("95% CI$", trimws(res_df$Group)), "0"][1]
+  # gtsummary separates the bounds with a non-breaking space; strip all
+  # non-numeric characters (except the separator and sign) before parsing
+  nums <- regmatches(val_ci, gregexpr("-?[0-9.]+", val_ci))[[1]]
+  got <- as.numeric(nums)
+
+  expect_equal(got, expected, tolerance = 1e-2)
+})
+
+test_that("annotate_lineplot_df ci label follows conf_level", {
+  mock_df2gg <- function(df, ...) df
+
+  res_df <- testthat::with_mocked_bindings(
+    {
+      annotate_lineplot_df(
+        gg_plt = p_valid,
+        data = mock_adlb,
+        summary_stats = c("mean", "ci"),
+        conf_level = 0.90
+      )
+    },
+    df2gg_aligned = mock_df2gg
+  )
+
+  expect_true(any(grepl("90% CI", trimws(res_df$Group))))
+})
+
 test_that("annotate_lineplot_df forwards font_size to df2gg_aligned", {
   captured <- NULL
   mock_df2gg <- function(df, ..., text_size, label_size) {
 
@@ -149,6 +149,53 @@ test_that("gg_lineplot converts character y to numeric automatically", {
   expect_s3_class(p_factor, "crane_gg_line")
 })
 
+test_that("gg_lineplot show_n appends group sizes to the legend", {
+  p <- gg_lineplot(
+    data = mock_adlb, x = AVISIT, y = AVAL, group = ARM,
+    stat = "mean", variability = "ci", show_n = TRUE
+  )
+
+  labels <- ggplot2::ggplot_build(p)$plot$scales$get_scales("colour")$get_labels()
+
+  # Treatment B has 30 rows, Treatment A has 20 (one visit removed above)
+  expect_true(any(grepl("Treatment A \\(N=20\\)", labels)))
+  expect_true(any(grepl("Treatment B \\(N=30\\)", labels)))
+})
+
+test_that("gg_lineplot show_n warns when no group is supplied", {
+  expect_warning(
+    gg_lineplot(
+      data = mock_adlb, x = AVISIT, y = AVAL,
+      stat = "mean", variability = "ci", show_n = TRUE
+    ),
+    regexp = "no effect when"
+  )
+})
+
+test_that("gg_lineplot jitter keeps points and error bars aligned", {
+  p <- gg_lineplot(
+    data = mock_adlb, x = AVISIT, y = AVAL, group = ARM,
+    stat = "mean", variability = "ci", jitter = 0.1
+  )
+  built <- ggplot2::ggplot_build(p)
+
+  # First layer is the points, last is the error bars; a shared seeded
+  # position must give them identical x coordinates
+  point_x <- sort(unique(round(built$data[[1]]$x, 4)))
+  ebar_x <- sort(unique(round(built$data[[length(built$data)]]$x, 4)))
+  expect_equal(point_x, ebar_x)
+})
+
+test_that("gg_lineplot validates jitter", {
+  expect_error(
+    gg_lineplot(
+      data = mock_adlb, x = AVISIT, y = AVAL, group = ARM,
+      variability = "ci", jitter = -1
+    ),
+    regexp = "must be a non-negative number"
+  )
+})
+
 test_that("gg_lineplot informs users about numeric vs categorical x-axis", {
   # 1. Numeric x-axis hits the final `else` branch (encourages using factor)
   expect_message(