The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

gtregression 1.2.0

New functions

risk_difference(): absolute risks and the risk difference between two groups for a binary outcome, as a gt or flextable table. The crude analysis uses the Miettinen-Nurminen score interval and score test. With adjust_for, risks are standardised over the analysis population from a main-effects logistic regression, and the Wald interval and p-value use the influence-function standard error. event_type = "harmful" or "beneficial" adds NNTB / NNTH with an Altman interval that runs through infinity when the risk-difference interval includes zero. The event and the direction of the contrast are always stated in the table footnote. modify_table() supports captions, caveats, labels and headers for these tables; merge_tables(), plot_reg(), plot_reg_combine(), forest_df() and forest_reg() reject them with a clear message.
conditional_logit_reg() for matched case-control studies: crude, adjusted and joint multivariable matched odds ratios, with matched-set accounting and informative-set, case and control counts.
multilevel_reg() for continuous and binary outcomes with one grouping variable and a random intercept: fixed-effect estimates, cluster and observation counts, random-intercept variance, intraclass correlation where appropriate, and convergence or singular-fit guidance. Random slopes and nested structures are outside its scope.
ordinal_reg() for ordered outcomes with three or more categories: crude, adjusted and joint multivariable proportional-odds models with common odds ratios, explicit outcome ordering, model-fit statistics and term-level proportional-odds checks.
multinomial_reg() for unordered outcomes with three or more categories. Each non-reference outcome comparison has its own column, and a standard regression object is kept per comparison for plots and forest tables.
competing_risk_reg() for Fine-Gray regression, with crude and adjusted SHR tables, and cuminc_plot() for cumulative incidence curves with explicit censoring and event-of-interest coding, confidence intervals, optional grouping and Gray’s test. The documentation explains how cumulative incidence, subdistribution hazards, cause-specific hazards and Kaplan-Meier estimates differ.
plot_cox_survival() draws adjusted survival curves and confidence bands from coxph or cox_reg() models.
import_data() imports CSV, TSV, TXT, Excel, RDS, Stata, SPSS and SAS files through one validated interface, keeps labelled columns, and uses optional readers only when they are installed. The app uses the same importer.
New dataset data_competing_risk, a fully synthetic oncology teaching dataset for competing-risk analysis.

Extended functions

cox_reg() supports delayed entry (start-stop data), clustered robust variance, case weights, tie handling and Cox fitting controls. baseline_strata stratifies the baseline hazard on one or more categorical variables while estimating common hazard ratios; no hazard ratio is reported for those variables and a footnote says so. It is distinct from stratifier, which fits separate models within each stratum.
check_ph() keeps its Schoenfeld-residual diagnostics and supports plot(), including model and term selection.
The new model families work with modify_table(), merge_tables(), plot_reg(), plot_reg_combine(), forest_df(), forest_reg() and the export helpers where statistically appropriate. plot_reg_combine() accepts crude and adjusted Fine-Gray, conditional logistic, ordinal and individual multinomial results, with model-specific axis labels.
forest_df() / forest_reg(): effect columns use the same header as the source table, so conditional_logit_reg() shows “Matched OR” and ordinal_reg() “Common OR”. Exposure-specific adjusted results (adjust_for) can be shown next to the single multivariable model of the same family, labelled “Mutually adjusted”. forest_df(multi = ) works without uni. Infinite confidence limits are kept and shown with the infinity symbol. Default axes use rounded ticks spaced so that labels do not overlap; xlim and ticks_at still override them.
forest_reg() works with forestploter 1.2.0 (axis settings through set_xaxis(), labels through set_labs(), themes through style) and remains compatible with forestploter 1.1.x. The ineffective effects argument is kept only as a deprecated compatibility argument.
merge_tables() handles sensitivity-analysis layouts, including several univariable or several multivariable tables with user-defined headings, with stable row alignment and each table’s own notes.

Changes that can alter numbers

Linear models: confidence intervals in uni_reg(), multi_reg() and the stratified functions are now t-based (as confint.lm()), consistent with the reported p-values. Intervals are slightly wider in small samples. forest_df() recovers the standard error with the same t quantile.
Binary outcomes are handled the same way whether stored as integer, double, logical, factor or character: 0/1 and 1/2 codes are accepted, TRUE or the second level is the event, and a message names the event that was modelled. In stratified analyses the coding is decided once on the full data.
Log-binomial models use starting values when the default fit fails. A fit that does not converge, or ends on the boundary of the parameter space (fitted risks of 1), is reported as not fitted with a suggestion to use robust Poisson, on every R version. On recent R versions such a fit could previously be returned as an ordinary estimate.
cox_reg(), surv_reg() and competing_risk_reg(): unused factor levels are dropped per fitted model, so Ref. is the level the model actually used and unused levels no longer appear as NA rows. Ordered factors are reported as level-versus-reference effects; estimates for ordered-factor exposures therefore change from polynomial to treatment contrasts. Treatment contrasts are also used for display in the other regression functions.
cox_reg(baseline_strata = ) builds the model with strata() so that the baseline hazard is stratified on every supported version of survival.
Stratified survival tables: crude N and Events are taken from the fitted models (they could be overstated with missing time_start, weights or cluster values). surv_model_compare() and surv_reg(model_stats = TRUE) count events on the rows used by the model.
select_models() fits every candidate on one complete-case sample, returns n_used and n_excluded, and backward selection can reach the intercept-only model.
identify_confounder(): the interaction p-value is a likelihood-ratio test for all approaches; numeric-coded candidates with at most 10 distinct values are treated as categories; unstable strata are ignored; the recommendation can be "not_estimable".
interaction_models() with approach = "robpoisson" uses a robust Wald test.
compare_models(): the change from the first model is computed on the ratio scale for ratio measures.
mediation_analysis(): the confidence-interval label follows conf_level, invalid exposure values are an error, failed bootstrap replicates give a warning, and a footnote states the logit approximation.
descriptive_table(): Date and date-time variables are excluded with a message; logical and constant variables are shown as categories; continuous variables with missing values gain a (Missing) row; numeric levels are sorted numerically; an unknown value is an error. A by variable that is also listed in exposures is left out of the summarised variables, with a message.

Changes to table wording and layout

show_ref = FALSE hides the reference row only for binary predictors; categorical predictors with more than two levels keep their reference category. The same rule applies to regression, stratified and merged tables, regression plots and forest plots.
uni_reg() writes confidence intervals with an en dash, as all other tables already did, and the N column is in the same position in all regression tables.
uni_reg() keeps exposures whose model could not be fitted: the row shows a dash, a footnote names them, and the result gains failed_exposures.
Non-estimable coefficients are shown as blank cells (previously NA (NA-NA)) in single, stratified and merged tables.
Reference and comparison labels are aligned at the same indentation in all regression tables, so binary rows such as No and Yes line up.
Interaction rows are labelled from the fitted model, so factor levels that contain : and duplicated coefficient names are displayed correctly.
Numeric stratifiers are ordered by value. The Stratified by: footnote shows the variable label when the stratifier has one.
cox_reg() and surv_reg() with stratifier: show_sample now defaults to "both" (N and events); it has no effect without stratifier. The N footnote shows a range with an en dash when models differ, uses “analysis intervals” with time_start, and the adjustment footnote uses variable labels. New footnotes report rows excluded for a missing stratifier and reference categories that differ between strata.
Non-stratified cox_reg(), surv_reg() and competing_risk_reg() tables end with a Number of events: footnote. New footnotes, when applicable, name the event level of a factor or character event and state that robust variance was used with case weights.
Multivariable tables of the new model families show an N column and its footnote. Their adjustment footnotes follow labels changed with modify_table() and can be removed before adding a custom caveat.
rmst_table(): confidence-interval headers follow conf.level; a warning and a footnote are given when tau exceeds the follow-up of a group.
merge_tables(): a variable shown as a single row in one table and with level rows in another is aligned on one block (with Ref. marked where the reference level differs); a continuous variable with a (Missing) row stays on one row; column labels, spanners, column order and footnotes are taken from the source tables (source spanners become an inner spanner row); abbreviation notes are combined into one line. Merged flextables use publication-style header and outer rules without rules between exposure rows.
modify_table() starts from the notes, labels, spanners and column order of the source table, so calling it without arguments leaves a table unchanged. remove_N_obs = TRUE removes every wording of the complete-observation footnote; remove_N = TRUE removes the N column and the notes that only explain that column.
Single-table flextables centre their value columns, as gt, descriptive, stratified and merged tables do. Compact themes ("clinical", "striped", "jama") keep the indentation of level rows.
DOCX export fits tables to the page width the same way on every operating system and flextable version.
plot_reg() and plot_reg_combine() place every estimate on the row of its own variable and level, including relabelled levels, adjusted-only levels and interaction rows. Without the ggtext package the axis labels are plain text.
km_plot(): the curve and the risk table share the same x limits. cuminc_plot(): xlim/ylim zoom without dropping data and groups follow the factor level order. plot_cox_survival() supports models with baseline_strata.

New messages and errors

A variable can no longer take two roles unnoticed. cox_reg() and surv_reg() refuse the time or event variable as an exposure or adjustment variable; identify_confounder(), interaction_models() and select_models() refuse the outcome as a predictor; interaction_models() refuses an effect modifier equal to the exposure or covariates repeating either; mediation_analysis() refuses covariates that repeat the exposure, mediator or outcome; km_plot(), survival_summary() and logrank_test() refuse a by variable equal to the time or event variable.
An invalid theme is an error in every table function and in merge_tables().
Survival data without censored observations are accepted. weights must be greater than zero; surv_reg() requires time greater than zero; ties = "exact" cannot be combined with time_start.
cox_reg(baseline_strata = ) checks for overlap with time, event, exposures, adjust_for and stratifier, and warns about strata with very small samples or no events.
Clear errors for logrank_test() without by, cuminc_plot() limits that are not increasing, competing_risk_reg() / cuminc_plot() without event_of_interest, plot_cox_survival(by = ) naming a baseline-strata variable, and surv_predict() on stratified input.
multinomial_reg() names the exposure and level in the error raised when a level is lost in the fitted sample.
modify_table() gives a clear error for mediation_analysis() results.
save_table() explains the alternatives when a flextable is saved as PDF.
The survival functions use each exposure once when it is listed more than once.

New result fields

uni_reg(): failed_exposures. select_models(): n_used, n_excluded.
Stratified results (stratified_uni_reg(), stratified_multi_reg(), stratified cox_reg() / surv_reg()) and rmst_table(): footnotes.
multinomial_reg(): a single format, plus column_labels, spanners and theme. Merged tables: inner_spanners and theme.
plot_cox_survival(): plot_data gains profile and stratum.

Variable-name arguments

A bare name that is a column of data always refers to that column, even when an object of the same name exists in the workspace or in a package (for example time, rank, class, by).
The one exception is a wrapper function that forwards its own argument, for example function(d, by) descriptive_table(d, "x", by = by): the value passed by the caller is used. This applies to all variable arguments, including time, event, stratifier, weights and by in the survival functions.
import_data() falls back to Latin-1 when a text file is not valid UTF-8, and no longer treats ' as a quote character.

gtregression app

The RStudio add-in is listed as Regression without code (gtregression app) in the Addins menu, so its purpose is clear next to other add-ins. It opens the same app as gtregression_app().
New Trials task for risk_difference(), beside Regression and Survival: choose the outcome and which value is the event, the treatment variable and its reference group, optional adjustment variables, and whether the number needed to treat is reported. The table, DOCX / RTF / HTML export, R code and Session Code come from risk_difference() itself.
More models (ordinal, multinomial, multilevel and matched case-control): exposure-model and multivariable results are kept separately, as in the Cox and parametric-survival workflow, and are available to Modify, Merge, plots, forest plots, downloads and Session Code. Multinomial results are offered per outcome comparison, and combined plots only pair results for the same comparison. Model statistics and ordinal assumption checks state which result is being inspected.
Errors never close the app or open the debugger: every error is shown as a short, plain-language notification, and failed downloads are explained the same way.
A variable chosen for two roles (for example a stratifier that is also an exposure) is explained in a dialog with a one-click fix that removes the extra selection and runs the analysis again; the Stratify panel warns as soon as the stratifier is also a model variable. A variable chosen as the outcome, time, event, grouping or treatment variable cannot be ticked again as a predictor in that workspace, and is removed from the list with a short note if it was already ticked. Select all skips the stratifier.
Before a model is run, the ready line lists things worth checking: rows lost to missing values, an outcome that does not suit the approach, variables with one value or many categories, numeric codes that may be categories, categories with all or no events, and few events per model term.
Model warnings and analysis messages (for example separation, non-convergence, or which outcome level was modelled) are shown in plain language above the table instead of only in the R console. A result is marked as out of date when its selections or data change, and each Run button opens the tab that shows its result.
A “Working…” indicator with elapsed time appears during longer runs, and Run buttons wait until the current run finishes.
Import problems (separator, encoding, empty file, sheet, missing reader package) are explained, and imports note empty columns, adjusted column names and unread Excel sheets.
The app keeps every table produced in a session. Earlier runs stay available to Merge, Modify, regression plots and forest plots (labelled with their run time), and stratified and risk-difference tables appear in Modify (stratified tables also in Merge). Session Code recreates every kept result from the selections used to run it, each under its own name (for example multi_result_1, multi_result). A session keeps up to 30 results in the desktop app and 15 on a web server; when fitted models exceed 500 MB (200 MB on a web server) the oldest results keep their table but release their models. Session Code lists the kept results and can remove earlier ones.
Session Code has Save session and Open a saved session, which keep the analysis data and selections in a .gtrsession file.
Generated R code quotes values the same way on every operating system, and values containing an apostrophe produce valid code. The generated merge code now runs as written (a line break was missing before save_table()).

Documentation

New articles “Risk Difference and NNT”, “Merge and Customise Tables” (three or more tables, themes and flextable styling), “Special Study Designs” and “Which Function Should I Use?”.
The website has a new home page, a grouped guides menu and a function reference organised by task. Reference titles use consistent wording (for example “Univariable regression”).

gtregression 1.1.0

Archived release: https://doi.org/10.5281/zenodo.22049943

Added

Added an internal Data Prep workflow to the gtregression app, with explicit original/prepared analysis-data selection, quick starts, undo/redo, confirmed reset, prepared-data download, and reproducible preparation code. This code is included directly in gtregression and does not add gtstats as a dependency.
Added observed-level reference category controls for categorical regression predictors in the app. Selected baselines are applied with reproducible factor releveling and included in generated and exported R scripts.
Added publication-ready flextable output as the default table format across package table functions, while retaining gt and tibble-style outputs where appropriate.
Added support for unquoted option values in common arguments, including model approaches and output formats, so users can write calls such as approach = logit and format = flextable.
Added adjust_for support for adjusted multivariable workflows, including downstream regression plots and combined regression plots.
Added adjusted-variable notes to regression plots and combined regression plots.
Added optional model-fit statistics for uni_reg() and multi_reg() via model_stats = TRUE, with AIC, BIC, log-likelihood, deviance, pseudo R-squared, linear-model R-squared, and model N stored in $model_stats.
Added approach = "firth" / approach = firth for Firth penalized logistic regression in uni_reg(), multi_reg(), stratified_uni_reg(), and stratified_multi_reg(), with OR tables and compatibility with regression plots and forest plot helpers.
Added data_endometrial, a classic endometrial cancer dataset with a separation pattern, for teaching and testing Firth logistic regression.
Added cox_reg() for Cox proportional hazards regression using time, event, exposures, and optional adjust_for, returning HR and adjusted HR tables.
Added surv_reg() for parametric survival regression using time, event, exposures, optional adjust_for, and a selectable survival distribution, returning time-ratio tables.
Added stratified Cox and parametric survival workflows through the stratifier argument in cox_reg() and surv_reg(), with stratum-specific N, event counts, and formatted regression tables.
Added km_plot() for Kaplan-Meier survival curves with optional confidence intervals, censoring marks, log-rank p-values, and number-at-risk tables.
Added more publication controls to km_plot(), including y-axis limits, percentage-scale display, optional grid removal, theme selection, confidence interval styling, and title/subtitle sizing for patchwork-style figure panels.
Added km_risk_table() for standalone Kaplan-Meier risk tables at requested follow-up times, with at-risk, event, and censored counts.
Added rmst_table() for restricted mean survival time summaries up to a chosen follow-up time, with optional two-group RMST difference.
Added surv_model_compare() for comparing candidate parametric survival distributions by AIC, BIC, log-likelihood, scale, events, and N.
Added plot_surv_fit() for visually comparing observed Kaplan-Meier curves with fitted parametric survival curves.
Added surv_predict() for model-based survival probability predictions from fitted parametric survival regression models.
Added survival_summary() for Kaplan-Meier median survival summaries with total N, events, censored counts, and publication-style table outputs.
Added survival_quantiles() for Kaplan-Meier survival time quantiles, including event percentiles, corresponding survival probabilities, and confidence intervals.
Added survival_prob() for Kaplan-Meier survival probabilities at fixed follow-up times, with at-risk, event, censored, and confidence interval columns.
Added logrank_test() for formal comparison of Kaplan-Meier survival curves, with observed and expected events plus formatted p-value output.
Added check_ph() for proportional hazards screening of Cox models using Schoenfeld residual tests, with flextable, gt, and tibble outputs.
Added plot_model_fit() for visual model diagnostics from fitted lm/glm models and from models stored in uni_reg() and multi_reg() outputs.
Added mediation_analysis() for regression-based mediation analysis with formatted direct, indirect, total, and proportion mediated tables.
Added plot_mediation() for drawing a mediation path diagram from mediation_analysis() outputs.
Added data_diabetes_mediation, a health-related diabetes teaching dataset for practicing obesity, glucose, and diabetes mediation workflows.
Added compare_models() for publication-ready comparison of fitted gtregression model outputs, including AIC, BIC, log-likelihood, likelihood ratio statistics, primary exposure estimates, percent change, analysis-sample checks, and highlighted best-fit summaries.
Added a guided candidate-model builder to the app’s Compare Models workflow. Users can name and fit two to six logistic, linear, count, Cox, or parametric survival candidates with model-specific exposures, adjustment variables, and optional interactions before producing a reproducible comparison table and complete copyable R code.
Added save_forest() for exporting forest_reg() outputs with reproducible sizing across graphics devices and operating systems.
Added automatic support for variable label attributes, including labels set with labelled::var_label(), across descriptive, regression, stratified, merged, plotted, and forest-style outputs.
Added formatted display outputs for diagnostic and review functions including dissect(), select_models(), interaction_models(), and identify_confounder().
Added Mantel-Haenszel comparison support to identify_confounder() to support confounding assessment alongside crude and adjusted model comparisons.
Added manual case-study scripts under dev/manual-tests/ for real-time testing of logistic, linear, log-binomial, robust Poisson, Poisson, negative binomial, Cox, parametric survival, Firth, and mediation workflows.

Changed

Improved descriptive_table(), uni_reg(), multi_reg(), stratified_uni_reg(), and stratified_multi_reg() documentation with clearer examples using package datasets.
Improved pkgdown organization, homepage content, reference grouping, and rendered examples to better show publication-ready tables and plots.
Improved README and pkgdown documentation with an explicit dependency overview showing the trusted R packages used for modelling, tidying, tables, Word export, figures, diagnostics, and forest plots.
Improved merge_tables() so descriptive, crude, and adjusted tables can be combined more reliably, including when visible variable labels differ between input tables.
Improved modify_table() so merged tables retain clean headers, spanners, and footnotes after relabelling.
Improved forest_df() and forest_reg() support for descriptive summaries combined with univariable and multivariable regression outputs.
Improved forest_df() and forest_reg() support for stratified regression outputs, including one-object stratified forest plots with highlighted stratum headers and preserved row order.
Improved downstream survival support so cox_reg() and surv_reg() outputs work with plot_reg(), plot_reg_combine(), forest_df(), forest_reg(), merge_tables(), modify_table(), and select_models().
Improved cox_reg() and surv_reg() consistency with the rest of the package: both now support single multivariable models, adjusted exposure workflows, interaction terms, stratified workflows, and coherent table labels.
Improved compare_models() output so user-supplied or object-derived model names are displayed instead of generic model labels, and context-aware warnings distinguish same-sample comparisons from different-sample comparisons.
Improved save_table() handling for wide Word tables by preferring landscape orientation before reducing font size, respecting minimum font sizes, and allowing users to turn width fitting off.
Improved table footer and source-note spacing across formatted tables so abbreviations, adjustment notes, and caveats render more compactly in flextable, gt, Word, and pkgdown outputs.
Improved Word export so flextable outputs are fitted to a standard Word page width by default, with save_docx(table_width = ...) available for custom document layouts.
Improved select_models() output so formatted tables clearly report the model selection direction used.
Improved repository organization for CRAN readiness, including excluding development-only manual scripts from package builds.

Fixed

Fixed merge_tables() flextable headers so internal merge suffixes such as _p1, _p2, and _p3 are not shown in rendered tables.
Fixed merge_tables() row alignment when descriptive and regression tables contain the same variables but different visible labels.
Fixed modify_table() handling of merged flextable outputs so clean subheaders and group spanners are preserved.
Fixed check_convergence() so failed multivariable model fits return a clear non-converged table rather than failing during table formatting.
Fixed pkgdown reference metadata for internal print methods.
Fixed save functions so files are written to a temporary directory when users do not provide an explicit destination, supporting CRAN-safe examples and tests.
Fixed univariable Cox regression preprocessing so each exposure is fitted on complete cases for time, event, and the current exposure rather than using a single complete-case dataset across all exposures.
Fixed Cox and parametric survival validation so zero follow-up times are allowed when accepted by the underlying survival model functions, while negative follow-up times remain invalid.
Fixed regression and publication-ready outputs so user-supplied variable order is preserved rather than alphabetically reordered.
Fixed forest plot merging so adjusted reference rows are left blank for variables that were not included in the adjusted model.
Fixed forest_df() row-order handling after joins and merges so forest plots follow the same display order as the source regression table.
Replaced CRAN-incompatible diabetes demo data that depended on removed mlbench Pima data with data_SynthDiabetes, a synthetic replacement based on mlbench::SynthDiabetes2 (mlbench >= 2.1-11), and regenerated all mediation teaching fixtures that depended on the prior dataset.

gtregression 1.0.0

Initial release of gtregression.
Added support for logit, log-binomial, linear, Poisson, robust Poisson, and negative binomial models.
Added functions for confounder identification, model selection, diagnostics, plotting, and saving output.
Added forest plot functions (plot_reg(), plot_reg_combine()).
Added initial documentation and pkgdown homepage.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.