In ellessenne/rsimsum: Analysis of Simulation Studies Including Monte Carlo Error

options(width = 150)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center", fig.width = 7,
  out.width = "80%"
)

As of version 0.6.0, rsimsum supports the fully automated creation of nested loop plots (Rücker and Schwarzer, 2014).

library(rsimsum)

A dataset that can be purposefully used to illustrate nested loop plots is bundled and shipped with rsimsum:

data("nlp", package = "rsimsum")

This data set contains the results of a simulation study on survival modelling with 150 distinct data-generating mechanisms:

head(nlp)

Further information on the data could be find in the help file (?nlp).

We can analyse this simulation study using rsimsum as usual:

s <- rsimsum::simsum(
  data = nlp, estvarname = "b", true = 0, se = "se",
  methodvar = "model", by = c("baseline", "ss", "esigma")
)
s

Finally, a nested loop plot can be automatically produced via the autoplot method, e.g. for bias:

library(ggplot2)
autoplot(s, type = "nlp", stats = "bias")

However:

1. Nested loop plots are suited for several DGMs but not for several methods;
2. The decision on how to nest the results is subjective - the top-level of nesting receives most emphasis;
3. It gives an overall impression, without focusing too much on details;
4. It is cumbersome to incorporate Monte Carlo errors in the plot.

References

• Rücker, G. and Schwarzer, G. 2014 Presenting simulation results in a nested loop plot. BMC Medical Research Methodology 14(1) <doi:10.1186/1471-2288-14-129>

ellessenne/rsimsum documentation built on March 10, 2024, 1:21 p.m.