Visualizing meta-analytic data with R package metaviz

Forest plot variants: rainforest plot and thick forest plots

In addition to traditional forest plots, rainforest plots as well as thick forest plots can be created via the variant argument. Alternatively, the wrapper functions viz_thickforest and viz_rainforest can be used with identical results. Rainforest and thick forest plots are two variants and enhancements of the classical forest plot recently proposed by Schild and Voracek (2015). Both variants visually emphasize large studies (with short confidence intervals and more weight in the meta-analysis), while small studies (with wide confidence intervals and less weight in the meta-analysis) are visually less dominant. For further details see help(viz_rainforest) and help(viz_thickforest).

viz_forest(x = mozart[1:10, c("d", "se")], study_labels = mozart[1:10, c("study_name")],
           summary_label = "Summary effect", xlab = "Cohen d", variant = "rain")

viz_forest(x = mozart[1:10, c("d", "se")], study_labels = mozart[1:10, c("study_name")],
           summary_label = "Summary effect", xlab = "Cohen d", variant = "thick", method = "FE")

The method argument controls the meta-analytic model (fixed effect or random effects model). Setting method from a fixed effect to a random effects model changes the estimated summary effect and meta-analytic (inverse-variance) weights assigned to each study accordingly.

viz_forest(x = mozart[1:10, c("d", "se")], study_labels = mozart[1:10, c("study_name")],
           summary_label = "Summary effect", xlab = "Cohen d", variant = "rain", method = "DL")

Subgroup analysis

The viz_forest function is able to use a categorical moderator variable to visualize a subgroup analysis. This is done via the group argument, a factor which corresponds to the subgroup membership of each study. We use the dichotomous moderator rr_lab to compute and visualize separate meta-analyses. In the case of subgroup analysis, the summary_label argument can be a vector containing names for all subgroups, arranged in the order of the levels of group.

viz_forest(x = mozart[1:10, c("d", "se")], 
           group = mozart[1:10, "rr_lab"], 
           study_labels = mozart[1:10, "study_name"], 
           summary_label = c("Summary (rr_lab = no)", "Summary (rr_lab = yes)"), 
           xlab = "Cohen d",
           col = "Greys",
           variant = "rain")

Available forest plots types: Cumulative and leave-one-out analysis.

Different aspects of meta-analytic data can be shown in forest plots. Within function viz_forest the type parameter controls which aspects are shown. Two examples are given below.

Argument type = "cumulative" shows a cumulative meta-analysis, that is, meta-analytic summary effects are computed sequentially by adding each study one-by-one.

viz_forest(x = mozart[, c("d", "se")], 
           group = mozart[, "rr_lab"], 
           study_labels = mozart[, "study_name"], 
           summary_label = c("Summary (rr_lab = no)", "Summary (rr_lab = yes)"), 
           xlab = "Cohen d",
           variant = "thick",
           type = "cumulative")

Argument type = "sensitivity" shows a leave-one-out analysis. That is, for each study the meta-analytic summary effect is shown if that particular study is not considered in the computation of the summary effect.

viz_forest(x = mozart[1:5, c("d", "se")],
           study_labels = mozart[1:5, "study_name"], 
           xlab = "Cohen d",
           variant = "rain",
           type = "sensitivity")

Aligning tables with study and/or summary information

The viz_forest function was developed to allow aligning a table to the forest plot containing study or summary information. First, textual annotations of effect sizes and confidence intervals can be optionally displayed with the argument annotate_CI = TRUE.

viz_forest(x = mozart[1:10, c("d", "se")], 
           group = mozart[1:10, "rr_lab"],
           study_labels = mozart[1:10, "study_name"], 
           summary_label = c("Summary (rr_lab = no)", "Summary (rr_lab = yes)"), 
           xlab = "Cohen d", 
           variant = "thick",
           annotate_CI = TRUE)

Second, arbitrary study information can be supplied as data.frame with the argument study_table. This data.frame can contain several variables to be aligned as columns and should contain one row for each study in the meta-analysis.

To illustrate, we might want to align a table with the study identifiers and number of events observed in each study of data set exrehab.

study_table <- data.frame(
  name = exrehab[, "study_name"],
  eventsT = paste(exrehab$ai, "/", exrehab$ai + exrehab$bi, sep = ""),
  eventsC = paste(exrehab$ci, "/", exrehab$ci + exrehab$di, sep = ""))
head(study_table)

##              name eventsT eventsC
## 1 Shaw (NEDHP) 81 109/323 113/328
## 2        Lewin 92    9/58   18/58
## 3      Haskell 94  62/145  72/155
## 4       Englom 96  26/102   34/91
## 5  Hofman-Bang 99   19/46   21/41
## 6 Belardinelli 01   11/59   21/59

We might also want to include the sum of all events as summary information. This can be done with the summary_table argument. summary_table should be a data.frame with the number of rows equal to the number of subgroups.

summary_table <- data.frame(
    name = "Summary",
    eventsT = paste(sum(exrehab$ai), "/", sum(exrehab$ai + exrehab$bi), sep = ""),
    eventsC = paste(sum(exrehab$ci), "/", sum(exrehab$ci + exrehab$di), sep = ""))
head(summary_table)

##      name  eventsT  eventsC
## 1 Summary 407/1471 453/1388

The table_headers of study_table and summary_table can be specified with table_headers.

viz_forest(x = exrehab[, c("logrr", "logrr_se")], variant = "classic",
col = "Greys", xlab = "logRR", annotate_CI = T, 
study_table = study_table,
summary_table = summary_table,
table_headers = c("ID", "Events (T)", "Events (C)"))

The spacing of the forest plot and aligned tables can be customized by supplying a layout matrix via table_layout.

viz_forest(x = exrehab[, c("logrr", "logrr_se")], variant = "classic",
col = "Greys", xlab = "logRR", x_limit = c(-0.35, 0.05),
annotate_CI = T, type = "sensitivity",
study_table = data.frame(left_out = exrehab[, "study_name"],
                         remaining_N = sum(exrehab[, "n1i"] + exrehab[, "n2i"]) - 
                           (exrehab[, "n1i"] + exrehab[, "n2i"])),
summary_table = "None",
table_headers = c("Study left out", "N remaining", "log Risk Ratio [95% CI]"),
table_layout = matrix(c(1, 2, 3), nrow = 1))

Transforming the x axis labels

For some transformed effect sizes (e.g., log odds ratios, log risk ratios, or Fisher’s z) it is good practice to transform the labels of the x-axis, such that they display the effect sizes on their original scale (e.g., odds, ratios, risk ratios, or correlations). This can be conveniently done with the argument x_trans_function.

viz_forest(x = exrehab[, c("logrr", "logrr_se")], variant = "thick",
study_labels = exrehab[, "study_name"], col = "Greys", 
study_table = study_table,
summary_table = summary_table,
annotate_CI = T,
table_headers = c("ID", "Events (T)", "Events (C)"),
x_trans_function = exp, xlab = "RR")

Color highlighting single studies or groups of studies

For forest plots and thick forest plots it is possible to individually customize the color of studies by supplying a vector of colors to the col argument. The color of the summary effect(s) can be customized individually as well with the summary_col argument.

viz_forest(x = mozart[1:10, c("d", "se")], 
           group = mozart[1:10, "rr_lab"], 
           study_labels = mozart[1:10, "study_name"], 
           summary_label = c("Summary (rr_lab = no)", "Summary (rr_lab = yes)"), 
           xlab = "Cohen d",
           col = c("firebrick", "steelblue4")[mozart[1:10, "rr_lab"]],
           summary_col = c("firebrick", "steelblue4"))

Trim-and-fill analysis, Egger’s regression

Showing imputed studies by the trim and fill method, the adjusted summary effect, as well as egger’s regression line can help to visually assess funnel plot asymmetry.

viz_funnel(exrehab[, c("logrr", "logrr_se")], 
           contours_col = "Greys",
           trim_and_fill = TRUE, trim_and_fill_side = "right", 
           egger = TRUE)

Additional evidence contours

Additional evidence contours can help to assess the robustness of the meta-analytic result. These contours show the effect of one hypothetical new study (with a certain effect size and standard error) on the significance of the updated meta-analytic summary effect (given a certain method).

viz_funnel(mozart[1:10, c("d", "se")], sig_contours = FALSE, addev_contours = TRUE)

Sunset (power-enhanced) funnel plots

A novel variant of the funnel plot displays the power of studies to detect an effect of interest using a two-sided Wald test. The sunset (power-enhanced) funnel plot can be crated with the function viz_sunset. By default the meta-analytic summary effect (fixed effect model) is used as the underlying true effect for power computations.

viz_sunset(homeopath[, c("d", "se")])

Many options for viz_sunset are provided. For instance, continuous power contours can be drawn, a user-specified underlying true effect chosen, and the significance level for power calculations altered. See help(viz_sunset) for further details.

viz_sunset(homeopath[, c("d", "se")], true_effect = -0.3, sig_level = 0.1, power_contours = "continuous")