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

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>