Description Usage Arguments Details Author(s) References See Also Examples

Draw a bubble plot to display the result of a meta-regression.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

`x` |
An object of class |

`xlim` |
The x limits (min,max) of the plot. |

`ylim` |
The y limits (min,max) of the plot. |

`xlab` |
A label for the x-axis. |

`ylab` |
A label for the y-axis. |

`cex` |
The magnification to be used for plotting symbols. |

`min.cex` |
Minimal magnification for plotting symbols. |

`max.cex` |
Maximal magnification for plotting symbols. |

`pch` |
The plotting symbol used for individual studies. |

`col` |
A vector with colour of plotting symbols. |

`bg` |
A vector with background colour of plotting symbols (only
used if |

`lty` |
The line type for the meta-regression line. |

`lwd` |
The line width for the meta-regression line. |

`col.line` |
Colour for the meta-regression line. |

`studlab` |
A logical indicating whether study labels should be printed in the graph. A vector with study labels can also be provided (must be of same length as the numer of studies in the meta-analysis then). |

`cex.studlab` |
The magnification for study labels. |

`pos` |
A position specifier for study labels (see |

`offset` |
Offset for study labels (see |

`regline` |
A logical indicating whether a regression line should be added to the bubble plot. |

`axes` |
A logical indicating whether axes should be printed. |

`box` |
A logical indicating whether a box should be printed. |

`...` |
Graphical arguments as in |

A bubble plot can be used to display the result of a meta-regression. It is a scatter plot with the treatment effect for each study on the y-axis and the covariate used in the meta-regression on the x-axis. Typically, the size of the plotting symbol is inversely proportional to the variance of the estimated treatment effect (Thompson & Higgins, 2002).

Argument `cex`

specifies the plotting size for each individual
study. If this argument is missing the weights from the
meta-regression model will be used (which typically is a random
effects model). Use `weight="fixed"`

in order to utilise
weights from a fixed effect model to define the size of the plotted
symbols (even for a random effects meta-regression). If a vector
with individual study weights is provided, the length of this vector
must be of the same length as the number of studies.

Arguments `min.cex`

and `max.cex`

can be used to define
the size of the smallest and largest plotting symbol. The plotting
size of the most precise study is set to `max.cex`

whereas the
plotting size of all studies with a plotting size smaller than
`min.cex`

will be set to `min.cex`

.

For a meta-regression with more than one covariate. Only a scatter plot of the first covariate in the regression model is shown. In this case the effect of the first covariate adjusted for other covariates in the meta-regression model is shown.

For a factor or categorial covariate separate bubble plots for each group compared to the baseline group are plotted.

Guido Schwarzer [email protected]

Thompson SG, Higgins JP (2002),
How should meta-regression analyses be undertaken and interpreted?
*Statistics in Medicine*, **21**, 1559–1573.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
data(Fleiss93cont)
# Add some (fictitious) grouping variables:
Fleiss93cont$age <- c(55, 65, 52, 65, 58)
Fleiss93cont$region <- c("Europe", "Europe", "Asia", "Asia", "Europe")
meta1 <- metacont(n.e, mean.e, sd.e,
n.c, mean.c, sd.c,
data=Fleiss93cont, sm="MD")
mr1 <- metareg(meta1, region)
mr1
bubble(mr1)
bubble(mr1, lwd=2, col.line="blue")
mr2 <- metareg(meta1, age)
mr2
bubble(mr2, lwd=2, col.line="blue", xlim=c(50, 70))
bubble(mr2, lwd=2, col.line="blue", xlim=c(50, 70), cex="fixed")
# Do not print regression line
#
bubble(mr2, lwd=2, col.line="blue", xlim=c(50, 70), regline=FALSE)
``` |

```
Loading 'meta' package (version 4.8-4).
Type 'help(meta)' for a brief overview.
Mixed-Effects Model (k = 5; tau^2 estimator: DL)
tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.5041)
tau (square root of estimated tau^2 value): 0
I^2 (residual heterogeneity / unaccounted variability): 0.00%
H^2 (unaccounted variability / sampling variability): 1.00
R^2 (amount of heterogeneity accounted for): 100.00%
Test for Residual Heterogeneity:
QE(df = 3) = 2.0242, p-val = 0.5674
Test of Moderators (coefficient(s) 2):
QM(df = 1) = 3.6359, p-val = 0.0565
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.0329 0.4796 0.0686 0.9453 -0.9071 0.9729
regionEurope -1.1267 0.5909 -1.9068 0.0565 -2.2849 0.0314 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Mixed-Effects Model (k = 5; tau^2 estimator: DL)
tau^2 (estimated amount of residual heterogeneity): 0.2788 (SE = 0.6563)
tau (square root of estimated tau^2 value): 0.5280
I^2 (residual heterogeneity / unaccounted variability): 36.33%
H^2 (unaccounted variability / sampling variability): 1.57
R^2 (amount of heterogeneity accounted for): 0.00%
Test for Residual Heterogeneity:
QE(df = 3) = 4.7118, p-val = 0.1942
Test of Moderators (coefficient(s) 2):
QM(df = 1) = 0.8613, p-val = 0.3534
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt -6.3926 6.0913 -1.0495 0.2940 -18.3314 5.5461
age 0.0909 0.0979 0.9281 0.3534 -0.1011 0.2828
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

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