Visualise Regression Results • gtregression

Regression tables are the evidence. Plots are the quick read. Use km_plot(), km_risk_table(), rmst_table(), survival_summary(), survival_quantiles(), survival_prob(), logrank_test(), surv_model_compare(), plot_surv_fit(), surv_predict(), plot_reg(), plot_reg_combine(), forest_df(), and forest_reg() to visualise survival and regression results, inspect reference categories, and prepare figures for manuscripts or reports.

library(gtregression)
library(dplyr)

data("data_birthwt", package = "gtregression")
data("data_lungcancer", package = "gtregression")

birthwt_data <- data_birthwt |>
  mutate(
    race = factor(race, levels = c(1, 2, 3),
                  labels = c("White", "Black", "Other")),
    smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
    ht = factor(ht, levels = c(0, 1), labels = c("No", "Yes")),
    ui = factor(ui, levels = c(0, 1), labels = c("No", "Yes")),
    low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW")),
    ptl_cat = factor(ifelse(ptl > 0, "Yes", "No"), levels = c("No", "Yes")),
    ftv_cat = factor(case_when(
      ftv == 0 ~ "None",
      ftv == 1 ~ "One",
      ftv >= 2 ~ "Two or more"
    ), levels = c("None", "One", "Two or more"))
  )

birthwt_exposures <- c(
  "age", "lwt", "race", "smoke", "ht", "ui", "ptl_cat", "ftv_cat"
)

attr(birthwt_data$age, "label") <- "Maternal age"
attr(birthwt_data$lwt, "label") <- "Maternal weight"
attr(birthwt_data$race, "label") <- "Maternal race"
attr(birthwt_data$smoke, "label") <- "Smoking during pregnancy"
attr(birthwt_data$ht, "label") <- "Hypertension"
attr(birthwt_data$ui, "label") <- "Uterine irritability"
attr(birthwt_data$ptl_cat, "label") <- "Previous preterm labour"
attr(birthwt_data$ftv_cat, "label") <- "First trimester visits"

birthwt_desc <- descriptive_table(
  birthwt_data,
  exposures = birthwt_exposures,
  by = "low",
  show_overall = "last"
)
birthwt_uni <- uni_reg(
  birthwt_data,
  outcome = "low",
  exposures = birthwt_exposures,
  approach = "logit"
)
birthwt_multi <- multi_reg(
  birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat", "ftv_cat"),
  adjust_for = c("age", "lwt", "race"),
  approach = "logit"
)

lung_data <- data_lungcancer |>
  mutate(
    trt = factor(trt, levels = c(1, 2),
                 labels = c("Standard treatment", "Test treatment")),
    prior = factor(prior, levels = c(0, 10), labels = c("No", "Yes"))
  )

lung_surv <- surv_reg(
  data = lung_data,
  time = time,
  event = status,
  exposures = "trt",
  adjust_for = c("age", "karno"),
  distribution = weibull
)

Kaplan-Meier Curve

km_plot() gives the survival curve before regression modelling. Use it to show the observed survival experience by group, with optional confidence intervals, censoring marks, log-rank p-value, and number-at-risk table. If the curves sit near the top of the graph, use ylim = c(50, 100) on the default percentage scale, or ylim = c(0.5, 1) when y_percent = FALSE.

km_plot(
  data = lung_data,
  time = time,
  event = status,
  by = trt,
  break_time_by = 200,
  ylim = c(50, 100),
  title = "Kaplan-Meier Survival by Treatment"
)

When preparing a multi-panel figure, use compact titles and control the legend directly. The result remains a normal ggplot object when risk_table = FALSE, so it can be combined with patchwork.

km_treatment <- km_plot(
  data = lung_data,
  time = time,
  event = status,
  by = trt,
  risk_table = FALSE,
  title = "A. Treatment",
  title_size = 10,
  title_face = plain,
  legend_position = bottom,
  base_size = 10
)

km_prior <- km_plot(
  data = lung_data,
  time = time,
  event = status,
  by = prior,
  risk_table = FALSE,
  title = "B. Prior therapy",
  title_size = 10,
  title_face = plain,
  legend_position = bottom,
  base_size = 10
)

patchwork::wrap_plots(km_treatment, km_prior, ncol = 2) +
  patchwork::plot_layout(guides = "collect") &
  ggplot2::theme(legend.position = "bottom")

Risk Table

km_risk_table() gives the number at risk at selected follow-up times as a standalone table. This is useful when the risk table needs to be reported beside or underneath a Kaplan-Meier curve.

km_risk_table(
  data = lung_data,
  time = time,
  event = status,
  by = trt,
  times = c(0, 90, 180, 365)
)

Restricted Mean Survival Time

rmst_table() reports the average survival time up to a fixed follow-up point, called tau. This is useful when readers want an absolute survival-time summary, or when hazard ratios are difficult to explain.

rmst_table(
  data = lung_data,
  time = time,
  event = status,
  by = trt,
  tau = 365
)

Kaplan-Meier Summary

survival_summary() is the table companion to the Kaplan-Meier curve. It reports the number analysed, events, censored observations, and median survival with a 95% confidence interval. Use it when readers need the key survival numbers without reading them from the plot.

survival_summary(
  data = lung_data,
  time = time,
  event = status,
  by = trt
)

Survival Quantiles

survival_quantiles() reports detailed Kaplan-Meier time points. This is useful when the median alone is not enough, or when you want to show the 25th, 50th, and 75th percentile event times by group.

survival_quantiles(
  data = lung_data,
  time = time,
  event = status,
  by = trt
)

Survival Probabilities

survival_prob() reports Kaplan-Meier survival probability at fixed follow-up times. This is useful for clinically familiar summaries such as 90-day, 6-month, or 1-year survival.

survival_prob(
  data = lung_data,
  time = time,
  event = status,
  by = trt,
  times = c(90, 180, 365)
)

Log-Rank Test

logrank_test() formally compares Kaplan-Meier curves between groups. It is useful after the curve and summary table, but it does not give an effect size; use cox_reg() when a hazard ratio is needed.

logrank_test(
  data = lung_data,
  time = time,
  event = status,
  by = trt
)

Parametric Survival Model Comparison

surv_model_compare() helps users choose a candidate distribution before using surv_reg(). It fits the same model with Weibull, exponential, lognormal, and loglogistic distributions, then compares AIC and BIC.

surv_model_compare(
  data = lung_data,
  time = time,
  event = status,
  exposures = c("trt", "celltype"),
  adjust_for = c("age", "karno")
)

Parametric Survival Fit Plot

plot_surv_fit() is the visual companion to surv_model_compare(). It overlays the observed Kaplan-Meier curve with fitted parametric survival curves, so users can check whether a distribution that looks good by AIC/BIC also follows the observed survival pattern.

plot_surv_fit(
  data = lung_data,
  time = time,
  event = status,
  by = trt,
  distributions = c(weibull, lognormal),
  break_time_by = 200
)

Adjusted fitted curves can also be drawn. In that case, the curves are predicted at typical adjustment values, such as medians for numeric variables and the most common level for categorical variables.

plot_surv_fit(
  data = lung_data,
  time = time,
  event = status,
  by = trt,
  adjust_for = c(age, karno),
  distributions = loglogistic,
  xlim = c(0, 800)
)

Parametric Survival Prediction

surv_predict() turns a fitted parametric survival model into predicted survival probabilities at clinically useful follow-up times. This helps users move from a time-ratio table to a more direct statement such as predicted 90-day, 180-day, or 1-year survival for a profile.

surv_predict(
  model = lung_surv$models$trt,
  newdata = data.frame(
    trt = factor("Test treatment", levels = levels(lung_data$trt)),
    age = 60,
    karno = 70
  ),
  times = c(90, 180, 365)
)

If newdata is omitted, surv_predict() uses a typical profile from the model data, using medians for numeric variables and the most common level for categorical variables.

One Regression Plot

plot_reg() turns a uni_reg() or multi_reg() result into a forest-style plot. By default, categorical reference levels are shown and labelled as (Ref.); the caption explains the abbreviation. Variable labels set on the data are used automatically in the plot.

plot_reg(
  birthwt_uni,
  title = "Crude Associations With Low Birth Weight"
)

Adjusted Regression Plot

When the input comes from multi_reg(adjust_for = ...), the adjustment set is shown in the plot caption by default. This keeps the figure interpretable when it is copied into slides or a manuscript draft.

plot_reg(
  birthwt_multi,
  show_ref = FALSE,
  log_x = TRUE,
  title = "Adjusted Associations With Low Birth Weight"
)

Compact Binary Predictors

For Yes/No, 1/0, true/false, or similar binary predictors, set show_ref = FALSE to hide reference rows. Affirmative binary levels such as Yes are displayed as the exposure name itself, so the plot remains compact: smoke, ht, ui, and ptl_cat are easier to read than repeated variable: Yes labels.

plot_reg(
  birthwt_uni,
  show_ref = FALSE,
  title = "Crude Associations With Reference Rows Hidden"
)

Log Axis and Tick Marks

For ratio measures such as odds ratios, risk ratios, and incidence rate ratios, log_x = TRUE uses a log-scaled x-axis. If you do not provide tick marks, gtregression chooses sensible defaults around the null value of 1.

plot_reg(
  birthwt_uni,
  log_x = TRUE,
  title = "Crude Associations on a Log Scale"
)

You can still take full control of the visible axis range and tick marks.

plot_reg(
  birthwt_uni,
  show_ref = FALSE,
  log_x = TRUE,
  xlim = c(0.25, 12),
  breaks = c(0.5, 1, 2, 4, 8),
  title = "Crude Associations With Custom Axis"
)

Compare Crude and Adjusted Effects

plot_reg_combine() places crude and adjusted model results side by side. This is useful when a manuscript needs to show how adjustment changes the estimate. Axis limits and tick marks can be controlled separately for each side.

plot_reg_combine(
  tbl_uni = birthwt_uni,
  tbl_multi = birthwt_multi,
  show_ref = FALSE,
  log_x = TRUE,
  xlim_uni = c(0.25, 12),
  breaks_uni = c(0.5, 1, 2, 4, 8),
  xlim_multi = c(0.25, 16),
  breaks_multi = c(0.5, 1, 2, 4, 8),
  title_uni = "Crude Effects",
  title_multi = "Adjusted Effects"
)

Publication-Style Forest Table

forest_df() prepares the data. forest_reg() draws the forest table. This is the most manuscript-oriented plot when you want descriptive summaries and crude or adjusted estimates in the same figure.

forest_data <- forest_df(
  uni = birthwt_uni,
  multi = birthwt_multi,
  desc = birthwt_desc
)

forest_reg(forest_data, quiet = TRUE)

Fine-Tune Forest Tables

Wide forest tables combine descriptive summaries, crude estimates, adjusted estimates, and one or two forest plot panels. If the x-axis labels are crowded, control the axis range and tick marks with xlim and ticks_at.

Use a list when the table has crude and adjusted forest plot columns.

forest_reg(
  forest_data,
  xlim = list(c(0.25, 8), c(0.8, 25)),
  ticks_at = list(
    c(0.5, 1, 2, 4, 8),
    c(1, 2, 4, 8, 16)
  ),
  quiet = TRUE
)

If the plot panel itself looks too narrow or too wide, tune ci_col_width. This changes the blank spacer column that forestploter uses for drawing the confidence intervals. Larger values give the CI panel more room; smaller values make the overall table more compact.

forest_reg(
  forest_data,
  ci_col_width = c(18, 22),
  xlim = list(c(0.25, 8), c(0.8, 25)),
  ticks_at = list(
    c(0.5, 1, 2, 4, 8),
    c(1, 2, 4, 8, 16)
  ),
  quiet = TRUE
)

For publication export, use a wider graphics device or document canvas when the table includes several descriptive columns and two model columns.

You can also build and draw in one call.

forest_reg(
  uni = birthwt_uni,
  multi = birthwt_multi,
  desc = birthwt_desc,
  side = "left",
  quiet = TRUE
)

What To Inspect

plot_reg() returns a ggplot.
plot_reg_combine() returns a combined ggplot.
km_risk_table() reports at-risk, event, and censored counts at requested follow-up times.
rmst_table() reports restricted mean survival time up to a chosen time point.
show_ref = TRUE displays reference levels as (Ref.).
show_ref = FALSE hides reference levels; affirmative binary predictors are shown as compact bold exposure rows.
Adjustment variables from multi_reg(adjust_for = ...) are carried into plot captions.
log_x = TRUE uses log scaling for non-linear model effect measures.
forest_df() returns the plotting data frame.
survival_prob() reports survival probability at requested follow-up times.
surv_model_compare() compares parametric survival distributions before fitting final surv_reg() tables.
plot_surv_fit() overlays fitted parametric survival curves on the observed Kaplan-Meier curve.
surv_predict() reports model-based survival probabilities at user-specified follow-up times.
In forest_reg(), use xlim and ticks_at when x-axis labels overlap.
In forest_reg(), use ci_col_width when the CI plot panel is too narrow or too wide.
forest_reg() returns plot, data, input_data, and meta.