Author: Alessandro Gasparini
Date: 2026-04-21

# Nested loop plots

#### Alessandro Gasparini

#### 2026-04-21

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)
    #>   dgm  i model           b        se baseline  ss beta esigma pars
    #> 1   1  1     1  0.17119413 0.2064344        E 100    0    0.1  0.5
    #> 2   1  1     2  0.19822898 0.2048353        E 100    0    0.1  0.5
    #> 3   1 50     2 -0.03404229 0.2071766        E 100    0    0.1  0.5
    #> 4   1 82     1 -0.09263968 0.2040281        E 100    0    0.1  0.5
    #> 5   1 82     2 -0.05095914 0.2026813        E 100    0    0.1  0.5
    #> 6   1 33     1 -0.17013365 0.2038076        E 100    0    0.1  0.5

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")
    )
    #> 'ref' method was not specified, 1 set as the reference
    s
    #> Summary of a simulation study with a single estimand.
    #> True value of the estimand: 0
    #>
    #> Method variable: model
    #>  Unique methods: 1, 2
    #>  Reference method: 1
    #>
    #> By factors: baseline, ss, esigma
    #>
    #> Monte Carlo standard errors were computed.

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>