heter_density_plot {rnmamod} | R Documentation |
Visualising the density of two prior distributions for the heterogeneity parameter
Description
Creating the density plot of two prior distributions for the between-study variance (log-normal and location-scale t distributions) or between-study standard deviation (half-normal distribution).
Usage
heter_density_plot(
distr,
heter_prior1,
heter_prior2,
heter1 = "tau",
heter2 = "tau",
caption = FALSE,
x_axis_name = TRUE,
y_axis_name = TRUE,
title_name = NULL,
axis_title_size = 13,
axis_text_size = 13,
legend_title_size = 13,
legend_text_size = 13
)
Arguments
distr |
Character string indicating the prior distribution.
Set |
heter_prior1 |
A numeric vector with two values for the first prior
distribution: 1) the mean value and 2) the standard deviation. When
|
heter_prior2 |
A numeric vector with two values for the second prior
distribution: 1) the mean value and 2) the standard deviation. When
|
heter1 |
Character string indicating the heterogeneity parameter
for |
heter2 |
Character string indicating the heterogeneity parameter
for |
caption |
Logical to indicate whether to report a caption at the bottom
right of the plot. It is relevant only when |
x_axis_name |
Logical to indicate whether to present the title of x-axis
('Between-study standard deviation'). The default is |
y_axis_name |
Logical to indicate whether to present the title of y-axis
('Density'). The default is |
title_name |
Text for the title of the plot. |
axis_title_size |
A positive integer for the font size of axis title.
|
axis_text_size |
A positive integer for the font size of axis text.
|
legend_title_size |
A positive integer for the font size of legend
title. |
legend_text_size |
A positive integer for the font size of legend text.
|
Details
Use this function to inspect the shape of the distribution and the range of
between-study variance or standard deviation values before you define the
argument heter_prior
in run_model
) to run random-effects
network meta-analysis.
Turner et al. (2012), Turner et al. (2015), and Rhodes et al. (2016) provide predictive prior distributions for the between-study variance for a binary outcome, measured in the log-odds ratio scale, and a continuous outcome, measured in the standardised mean difference scale, respectively.
Value
A plot with the density of two selected prior distributions for the heterogeneity parameter. Two different colours are used to discern the distributions. A legend is also created with the name and hyper-parameters of the selected prior distributions. The filled area under each curved indicates the values up to the median of the corresponding distribution. The x-axis present the 0.1
heter_density_plot
also returns a table with the percentiles of each
distribution.
Author(s)
Loukia M. Spineli
References
Rhodes KM, Turner RM, Higgins JP. Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol 2015;68(1):52–60. doi: 10.1016/j.jclinepi.2014.08.012
Turner RM, Jackson D, Wei Y, Thompson SG, Higgins JP. Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis. Stat Med 2015;34(6):984–98. doi: 10.1002/sim.6381
Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JP. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol 2012;41(3):818–27. doi: 10.1093/ije/dys041
See Also
Examples
## Not run:
## Two empirical priors for between-study variance of log odds ratio.
heter_density_plot(distr = "lognormal",
heter_prior1 = c(-2.56, 1.74), # General healthcare setting
heter_prior2 = c(-1.83, 1.52)) # Pain and pharma vs. placebo/ctrl
## Two empirical priors for between-study variance of standardised mean
## difference.
heter_density_plot(distr = "logt",
heter_prior1 = c(-3.44, 2.59), # General healthcare setting
heter_prior2 = c(-0.60, 2.61)) # Pain and pharma vs. placebo/ctrl for cancer
## Two half-normal prior distributions for between-study standard deviation
heter_density_plot(distr = "halfnormal",
heter_prior1 = c(0, 1),
heter_prior2 = c(0, 0.5))
## End(Not run)