Example 1: simple forest plot
This example uses the dataset named df.OR distributed along
with the metaumbrella package. In this example, we plot the results of
the calculations of the umbrella review while no stratification of
evidence has been performed.
# perform the calculations
umb <- umbrella(df.OR, verbose = FALSE)
# plot the results
forest(umb)
The effect sizes are displayed as eG, the default in
this function. Since all factors have used OR, we can
convert the effect sizes in eOR using the measure argument.
Moreover, because all effect sizes are OR and not
eOR, we indicate it in the plot (we can do this using the
main_value argument to modify the title of the value column
and using the main_x_axis argument to modify the title of
the x-axis).
In the meantime, we also add a title to the plot using
the main_title argument.
forest(umb,
measure = "eOR", # display eOR instead of eG
main_value = "OR [95% CI]", # header of the column displaying the effect sizes
main_x_axis = "Odds Ratio (OR)", # title of the x axis
main_title = "Umbrella review of risk factors for\n neurodevelopmental disorders" # title of the plot
)
As you can see in the code for the title, we have added “backslash” n
after the word ‘for’. This forces a line break.
If you find that the
title is too large (or too small) compared to the plot, you can modify
its size using the cex_title argument. Here, we reduce its
size.
forest(umb,
measure = "eOR",
main_value = "OR [95% CI]",
main_x_axis = "Odds Ratio (OR)",
main_title = "Umbrella review of risk factors for\n neurodevelopmental disorders",
cex_title = 1.1, # size of the title, default set as 1.4
)
The size of the dots reflects the precision of the pooled effect
sizes. The narrower the 95% CI, the larger the size of the dot. If you
find that the size of the dots is too disproportionate between the
factors, you have two options.
- Rather than using our raw index of precision to scale the size of
the dots, you can take its natural logarithm (using the
log_cex_dots argument). This will automatically reduces the
variability in the size of the dots.
forest(umb,
measure = "eOR",
main_value = "OR [95% CI]",
main_x_axis = "Odds Ratio (OR)",
main_title = "Umbrella review of risk factors for\n neurodevelopmental disorders",
cex_title = 1.1,
log_cex_dots = TRUE # the natural log of the index of precision is used to scale the size of the dots
)
- If the dot representation still does not suit you, you can simply
request all the dots to have the same size. The larger the numeric value
provided in the
fix_size_dots argument, the larger the size
of the dots.
forest(umb,
measure = "eOR",
main_value = "OR [95% CI]",
main_x_axis = "Odds Ratio (OR)",
main_title = "Umbrella review of risk factors for\n neurodevelopmental disorders",
cex_title = 1.1,
fix_size_dots = 1.5 # fix the size of the dots
)
Example 2: modified forest plot (modification of column
positions)
This example uses two datasets named df.SMD and
df.HR distributed along with the metaumbrella package. For
the example, we stratify the evidence for all factors included in these
two datasets according to some Personalized criteria.
# perform the calculations
umb <- union.umbrella(umbrella(df.SMD), umbrella(df.HR))
# stratify the evidence
strat.prso <- add.evidence(umb, criteria= "Personalized",
class_I = c(n_studies = 10, total_n = 3000, egger_p = .10, esb_p = .05),
class_II = c(n_studies = 10, total_n = 2000, egger_p = .10),
class_III = c(n_studies = 10, total_n = 1000, egger_p = .10),
class_IV = c(n_studies = 10, total_n = 500, egger_p = .10))
# plot the results
forest(strat.prso)
As you can see, the results are ordered according to the class
reached. The meta-analyses using HR effect size measures
have been automatically converted to eG.
In some situations, you may be interested in modifying the position of
the
value column so that it is presented at the left of the
plot. To do so, you have to:
-
shift the ‘factor’ column to the left (via the
xlim_factor
argument) and
-
shift the ‘value’ column right to the left of the plot (via the
xlim_value argument).
However, before moving the ‘factor’ column to the left, you have
first to increase the size of the left area of the plot (via the
xlim argument)
forest(strat.prso,
xlim = c(-8, 4), # this increases the size of the left area of the plot
xlim_factor = -6 # this moves the factors column to the left
)
Once the ‘factor’ column has been moved, we can move the value column
using the xlim_value.
forest(strat.prso,
xlim = c(-8, 4),
xlim_factor = -6,
xlim_value = -3.5 # this moves the value column to the left
)
Putting the columns side by side makes clear that their centering is
different. By default, the factor column in right-aligned
while the value column is left-aligned.
Let’s start by
changing the alignment of the value column.
forest(strat.prso,
xlim = c(-8, 4),
xlim_factor = -6,
xlim_value = -3.5,
pos_value = "center" # change the alignment of the value column
)
Now, we can adjust the alignment of the factor column.
forest(strat.prso,
xlim = c(-8, 4),
xlim_factor = -6,
xlim_value = -3.5,
pos_value = "center",
pos_text = "center" # change the alignment of the factor column
)
The plot is now almost ready. To finish, we can spread slightly the
two columns and decrease the overall width of the figure by decreasing
the value of the x-limits of the plot using the xlim
argument.
forest(strat.prso,
xlim = c(-6.8, 2), # adjust the x-limits of the plot.
xlim_factor = -6,
xlim_value = -3.5,
pos_value = "center",
pos_text = "center"
)
Example 3: modified forest plot (addition of new columns)
This example uses several fictitious datasets distributed along with
the metaumbrella package, namely df.SMD,
df.OR, df.RR, df.IRR and
df.OR.multi. We stratify the evidence for all factors
included in these datasets according to the Ioannidis classification
# perform the calculations
umb <- union.umbrella(union.umbrella(
union.umbrella(union.umbrella(
umbrella(df.SMD), umbrella(df.OR)),
umbrella(df.RR)), umbrella(df.IRR)),
umbrella(df.OR.multi, mult.level = TRUE))
strat.prso <- add.evidence(umb, criteria = "Ioannidis")
forest(strat.prso)
In this example, we will see how to display more information on the
meta-analyses included in the umbrella review. Let’s imagine that you
are interested in plotting the name of the first author of the original
meta-analyses (stored in the meta-review column of the original
datasets) and the number of cases.
First, we need to extract the name of the first author of the
meta-analyses in the original dataset.
list_authors <- lapply(list(df.SMD, df.OR, df.RR, df.IRR, df.HR, df.OR.multi),
function(x) cbind(author = unique(x$meta_review),
factor = unique(x$factor)))
authors <- do.call(rbind, list_authors)
authors
## author factor
## [1,] "Smith" "Pharmacological"
## [2,] "Smith" "Surgical"
## [3,] "Garcia" "ADHD"
## [4,] "Garcia" "ASD"
## [5,] "Garcia" "dyslexia"
## [6,] "Garcia" "ID"
## [7,] "el-Hakim" "SSRI"
## [8,] "Pham" "Smoking"
## [9,] "al-Ishak" "Yoga"
## [10,] "al-Ishak" "Aerobic training"
## [11,] "al-Ishak" "Resistance training"
## [12,] "al-Ishak" "Mindfulness"
## [13,] "el-Can" "n-3 fatty acids"
## [14,] "Bown" "Protein"
## [15,] "el-Abood" "Carbohydrates"
## [16,] "Hinostroza" "Lipid"
## [17,] "al-Rasul" "Dietary fiber"
## [18,] "Ortega" "Sodium"
## [19,] "Nelson" "Zinc"
## [20,] "Huynh" "Vitamin D"
We now have the list of first authors of the original meta-analyses
associated with the different factors. However, in the plot, the factors
are ranked according to their class obtained in the stratification of
evidence. Therefore, they are not in the same order as the information
extracted. To reorganize this dataset, we need to use the information on
the order of the factors which is automatically returned by the
forest function.
order_factors_plot <- forest(strat.prso)
# this allows to obtain the order of the factors in the plot
order_factors <- order_factors_plot$factor$factor
# now that we have the correct order, we simply have to reorganize the dataset according to this information
authors_ordered <- authors[match(order_factors, authors[, "factor"]), ]
authors_ordered
## [1] "Pharmacological" "Smoking" "ID" "SSRI"
## [5] "Dietary fiber" "ADHD" "dyslexia" "Sodium"
## [9] "Vitamin D" "Zinc" "ASD" "Protein"
## [13] "n-3 fatty acids" "Surgical" "Carbohydrates" "Lipid"
## author factor
## [1,] "Smith" "Pharmacological"
## [2,] "Pham" "Smoking"
## [3,] "Garcia" "ID"
## [4,] "el-Hakim" "SSRI"
## [5,] "al-Rasul" "Dietary fiber"
## [6,] "Garcia" "ADHD"
## [7,] "Garcia" "dyslexia"
## [8,] "Ortega" "Sodium"
## [9,] "Huynh" "Vitamin D"
## [10,] "Nelson" "Zinc"
## [11,] "Garcia" "ASD"
## [12,] "Bown" "Protein"
## [13,] "el-Can" "n-3 fatty acids"
## [14,] "Smith" "Surgical"
## [15,] "el-Abood" "Carbohydrates"
## [16,] "Hinostroza" "Lipid"
The
authors_ordered object now stores the name of the
authors in the same order as in the plot. We simply have to add the
information to the plot.
-
The
add_columns argument allows to pass a vector (or a
dataframe) which will be added to the plot.
-
The
main_add_columns argument allows to specify the names
of the new columns (by default, the names of the columns are those used
in the input object).
-
The
xlim_add_columns argument allows to specify the
position of the new columns on the x-axis.
We will add these new columns on the left side of the plot. As in the
previous example, because we include more information on the left side
of the plot, we need to increase the width of the plot on the left
area.
# we create the variable displaying the number of studies and total sample size
sum <- summary(strat.prso)
# as you can see, the order of the summary function and of the forest function is identical. We do not need to reorder this information.
stud_total_n = paste0(sum$n_cases)
forest(strat.prso,
add_columns = data.frame(authors_ordered[, "author"], stud_total_n), # new columns
main_add_columns = c("Authors", "Studies\n(sample size)"), # name of the new columns
xlim_add_columns = c(-7.5, -5), # position of the new columns
xlim = c(-10, 4.5) # increase the width of the plot on the left side
)
Not too bad. We will now adjust the centering of the columns on the
left side of the plot using the pos_text argument.
forest(strat.prso,
add_columns = data.frame(authors_ordered[, "author"], stud_total_n),
main_add_columns = c("Authors", "Studies\n(sample size)"),
xlim_add_columns = c(-7.5, -5),
xlim = c(-10, 5),
pos_text = "center" # center the factors columns and the two columns added to the plot
)
The columns are now a bit too close to the plot. We will adjust their
x-axis positions.
forest(strat.prso,
add_columns = data.frame(authors_ordered[, "author"], stud_total_n),
main_add_columns = c("Authors", "Studies\n(sample size)"),
xlim_add_columns = c(-9.5, -7), # adjust the x-axis position of the new columns
xlim = c(-10, 5),
pos_text = "center",
xlim_factor = -4 # adjust the x-axis position of the factor column
)
If you find that the number of meta-analyses displayed is too high,
you can restrict to some classes. For example, you can be interested in
presenting factors reaching at least a class IV. This can be done using
the print.classes argument.
forest(strat.prso,
add_columns = data.frame(authors_ordered[, "author"], stud_total_n),
main_add_columns = c("Authors", "Studies\n(sample size)"),
xlim_add_columns = c(-9.5, -7),
xlim = c(-10, 5),
pos_text = "center",
xlim_factor = -4,
print.classes = c("I", "II", "III", "IV") # request to print only classes I to IV
)
Finally, if you find that the text in the columns and the dots are
too large compared to the plot, you can reduce their size.
forest(strat.prso,
add_columns = data.frame(authors_ordered[, "author"], stud_total_n),
main_add_columns = c("Authors", "Studies\n(sample size)"),
xlim_add_columns = c(-9.5, -7),
xlim = c(-10, 5),
pos_text = "center",
xlim_factor = -4,
print.classes = c("I", "II", "III", "IV"),
cex_text = 0.8, # reduce the size of the text of the factor and new columns
cex_value = 0.8,# reduce the size of the text of the value column
cex_text_header = 0.8, # reduce the size of the header of the factor and new columns
cex_value_header = 0.8, # reduce the size of the header of the value column
cex_dots = 0.8 # reduce the size of the squares
)
