Analyzing Gastric Emptying from MRI or Scintigraphy

Fits gastric emptying time series from MRI or scintigraphic measurements using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with Stan; computes derived parameters such as t50 and AUC.

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By [email protected], Menne Biomed Consulting Tübingen, D-72074 Tübingen

A package and a Shiny web application to create simulated gastric emptying data, and to analyze gastric emptying from clinical studies using a population fit with R and package nlme. Stan-based ( Bayesian fits that can handle critical cases are included and will be extended in future.

Part of the work has been supported by section GI MRT, Klinik für Gastroenterologie und Hepatologie, Universitätsspital Zürich; thanks to Werner Schwizer and Andreas Steingötter for their contributions.


The package is available from github, source, documentation. To install, use:


Compilation of the Stan models needs several minutes.

Shiny online interface

The web interface can be installed on your computer, or run as web app.

Two models are implemented in the web interface

  • linexp, vol = v0 * (1 + kappa * t / tempt) * exp(-t / tempt):Recommended for gastric emptying curves with an initial volume overshoot from secretion. With parameter kappa > 1, there is a maximum after t=0. When all emptying curves start with a steep drop, this model can be difficult to fit.
  • powexp, vol = v0 * exp(-(t / tempt) ^ beta): The power exponential function introduced by Elashof et. al. to fit scintigraphic emptying data; this type of data does not have an initial overshoot by design. Compared to the linexp model, fitting powexp is more reliable and rarely fails to converge in the presence of noise and outliers. The power exponential can be useful with MRI data when there is an unusual late phase in emptying.

Methods with variants

  • Population fits based on function nlme in package R nlme.
  • Stan-based fits, both without and with covariance estimation. Thanks to priors, fitting with Bayesian methods also works for single records, even if stability strongly improves with more data sets available; see stan_gastempt. Some details can be found in my blog. The rationale for using Stan to fit non-linear curves is discussed here for 13C breath test data, but is equally valid for gastric emptying data.

Data entry:

  • Data can be entered directly from the clipboard copied from Excel, or can be simulated using a Shiny app.
  • Several preset simulations are provided in the Shiny app, with different amounts of noise and outliers
  • Robustness of models can be tested by manipulating noise quality and between-subject variance.
  • Fits are displayed with data.
  • The coefficients of the analysis including t50 and the slope in t50 can be downloaded in .csv format.


Program with simulated data (needs about 40 seconds till plot shows):

dd = simulate_gastempt(n_records = 6, seed = 471)
d = dd$data
ret = stan_gastempt(d)


  • Post-hoc analysis in Shiny application by treatment groups, both for cross-over and fully randomized designs.



Reference manual

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0.4.01 by Dieter Menne, 9 months ago

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Browse source code at

Authors: Dieter Menne [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Rcpp, nlme, dplyr, tibble, ggplot2, rstan, assertthat, stringr, shiny

Suggests rmarkdown, knitr, covr, testthat, methods

Linking to StanHeaders, rstan, BH, Rcpp, RcppEigen

See at CRAN