Description Usage Arguments Details Value Author(s) References Examples

Creates a funnel plot with power regions and computes power-related statistics.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
viz_sunset(
x,
y_axis = "se",
true_effect = NULL,
method = "FE",
sig_level = 0.05,
power_stats = TRUE,
power_contours = "discrete",
contours = FALSE,
sig_contours = TRUE,
text_size = 3,
point_size = 2,
xlab = "Effect",
ylab = NULL,
x_trans_function = NULL,
x_breaks = NULL,
y_breaks = NULL,
x_limit = NULL,
y_limit = NULL
)
``` |

`x` |
data.frame or matrix with the effect sizes of all studies (e.g.,
log odds ratios, or Cohen |

`y_axis` |
character string indicating which y axis should be used in the funnel plot. Available options are "se" (default) for standard error and "precision" for the reciprocal of the standard error. |

`true_effect` |
numeric scalar. Which true effect should be assumed for power calculations? The default is |

`method` |
character string indicating the method used to compute the meta-analytic summary effect. Can be any method argument from |

`sig_level` |
logical scalar. For which significance level alpha should the study power be computed? |

`power_stats` |
logical scalar. Should power-related statistics be computed and printed in the caption of the plot? (see details) |

`power_contours` |
character string specifying how different power regions are plotted. Can be either "continuous" or "discrete" (default). |

`contours` |
logical scalar indicating if classic funnel plot confidence contours and the summary effect should be displayed. |

`sig_contours` |
logical scalar. Should significance contours be drawn (at the 0.05 or 0.01 level using a two-sided Wald test)? |

`text_size` |
numeric value. Size of text in the funnel plot. Default is 3. |

`point_size` |
numeric value. Size of the study points in the funnel plot. Default is 2. |

`xlab` |
character string specifying the label of the x axis. |

`ylab` |
character string specifying the label of the y axis. |

`x_trans_function` |
function to transform the labels of the x axis. Common uses are to transform
log-odds-ratios or log-risk-ratios with |

`x_breaks` |
numeric vector of values for the breaks on the x-axis. When used in tandem with |

`y_breaks` |
numeric vector of values for the breaks on the y-axis. |

`x_limit` |
numeric vector of length two with user specified x axis limits. |

`y_limit` |
numeric vector of length two with user specified y axis limits. |

The funnel plot is the most widely used diagnostic plot in meta-analysis, primarily to assess small-study effects. The sunset (power-enhanced) funnel plot incorporates study-level power information in the funnel display. This directly allows to examine the power studies had to detect an effect of interest (e.g., the observed meta-analytic summary effect), whether funnel plot asymmetry is driven by underpowered but significant studies, and to visually assess if there is an excess of low-powered significant effects in the meta-analysis (conceptually related to the test of excess significance, Ioannidis & Trikalinos, 2007). For effect sizes assumed to be normally distributed (e.g., Cohen d, log OR), the power corresponding to a given standard error is computed by using a two-sided Wald test and (by default) the meta-analytic summary effect as assumed true effect. Colored regions of different power levels and a second axis with study level power are shown in the funnel plot. In addition, power-related statistics are shown: a) The median power of all studies, b) the true effect size necessary such that the median power of the studies would have been 33% or 66%, c) results of a test of excess significance (Ioannidis & Trikalinos, 2007), and d) the R-Index for expected replicability (Schimmack, 2016).

A power enhanced ("sunset") funnel plot is created using ggplot2.

Michael Kossmeier* <michael.kossmeier@univie.ac.at>

Ulrich S. Tran* <ulrich.tran@univie.ac.at>

Martin Voracek* <martin.voracek@univie.ac.at>

*Department of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna

Ioannidis, J. P., & Trikalinos, T. A. (2007). An exploratory test for an excess of significant
findings. *Clinical Trials*, *4*, 245-253.

Schimmack, U. (2016). The replicability-index: Quantifying statistical research integrity. Retrieved from https://replicationindex.wordpress.com/2016/01/31/a-revised-introduction-to-the-r-index/

1 2 3 |

```
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.