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rmsMD

rmsMD provides streamlined functions for summarising and visualising regression models fitted with the rms package, in the preferred format for medical journal reporting.

It includes support for linear, logistic, and Cox models, handles restricted cubic splines (RCS) automatically, and produces publication-ready tables and plots.

To learn more about the concepts underlying non-linear regression modelling and restricted cubic splines, see our related publication, Non-linear regression modelling for medical professionals: making curved paths straightforward.

Installation

You can install rmsMD from CRAN:

It is recommended that this package is used alongside rms, which can be installed via:

Quick start

Fit regression models and create clean outputs

Fit a logistic regression model with restricted cubic splines for age and BMI.
Generate a publication-ready summary table from the fitted model.

library(rmsMD)
library(rms)

# Fit an OLS model including a restricted cubic spline 
# for Age and BMI (with 4 knots)
fit_lrm <- lrm(majorcomplication ~ rcs(age,4) + 
                 rcs(bmi,4) + 
                 sex + 
                 smoking, 
               data = data)

# Generate a publication-ready summary table
modelsummary_rms(fit_lrm)

Example output table

Building basic plots

Create basic plots of the model’s restricted cubic spline terms to visualize adjusted effects.

# Most basic output
ggrmsMD(fit_lrm, data)

Basic RCS plot

Plot customisation

Customize axis labels, plot titles, and shading to highlight inferior outcomes visually.

# x axis labels can be stored in a list
xlabels <- list ("age" = "Age (years)",
                 "bmi" = "Body Mass Index")

# titles for each variable can be stored in a list
titles <- list ("age" = "Impact of Age on Complications",
                 "bmi" = "Impact of BMI on Complications")

ggrmsMD(fit_lrm, data,
        # set y axis label for all plots
        ylab = "Complications (adjusted OR)", 
        # set y axis limits
        ylim = c(0,3),
        # set higher OR as inferior outcome to assign red shading
        shade_inferior = "higher", 
        # set x axis labels for each variable
        xlabs = xlabels, 
        # set titles for each variable
        titles = titles 
        )

Advanced RCS plot

Citation

If you use rmsMD in your work, please cite the following article:

Tingle SJ, Kourounis G, Elliot S, Harrison EM. Non-linear regression modelling for medical professionals: making curved paths straightforward. Postgrad Med J. 2025 Nov; qgaf183. DOI: 10.1093/postmj/qgaf183

BibTeX entry for reference managers:

@article{10.1093/postmj/qgaf183,
    author = {Tingle, Samuel J and Kourounis, Georgios and Elliot, Sarah and Harrison, Ewen M},
    title = {Non-linear regression modelling for medical professionals; making curved paths straightforward},
    journal = {Postgraduate Medical Journal},
    pages = {qgaf183},
    year = {2025},
    month = {11},
    issn = {0032-5473},
    doi = {10.1093/postmj/qgaf183},
    url = {https://doi.org/10.1093/postmj/qgaf183},
    eprint = {https://academic.oup.com/pmj/advance-article-pdf/doi/10.1093/postmj/qgaf183/65131196/qgaf183.pdf},
}

Learn more

See the Standard workflow with restricted cubic splines vignette for a full worked example.