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Travis build status

tidymeta

Tidy and plot meta-analyses from popular meta-analytic tools in R. Currently in early development.

Installation

tidymeta requires the development version of several packages, including ggplot2, to function correctly. You can install the required packages for this vignette with the following code:

install.packages(c("devtools", "yaml", "ggrepel"))
library(devtools)
install_github("r-lib/rlang")
install_github("malcolmbarrett/tidymeta")
install_github("malcolmbarrett/mbmisc")
install_github("tidyverse/ggplot2")

Tidy Meta-Analysis

tidymeta is a toolkit for working with meta-analyses in R. Currently, it includes a data set, called iud_cxca, for a meta-analysis of the relationship between IUD use and cervical cancer.

library(tidymeta)
library(ggplot2)
library(dplyr)
library(broom)

iud_cxca
#> # A tibble: 16 x 26
#>    study_id study_name    author    es    l95   u95    lnes  lnl95   lnu95
#>       <int> <chr>         <chr>  <dbl>  <dbl> <dbl>   <dbl>  <dbl>   <dbl>
#>  1        1 Roura, 2016   Roura  0.600 0.300  1.20  -0.511  -1.20   0.182 
#>  2        2 Lassise, 1991 Lassi… 0.800 0.500  1.20  -0.223  -0.693  0.182 
#>  3        3 Li, 2000      Li     0.890 0.730  1.08  -0.117  -0.315  0.0770
#>  4        4 Shields, 2004 Shiel… 0.500 0.300  0.820 -0.693  -1.20  -0.198 
#>  5        5 Castellsague… Caste… 0.630 0.380  1.06  -0.462  -0.968  0.0583
#>  6        6 Castellsague… Caste… 0.450 0.300  0.670 -0.799  -1.20  -0.400 
#>  7        7 Brinton, 1990 Brint… 0.690 0.500  0.900 -0.371  -0.693 -0.105 
#>  8        8 Parazzini, 1… Paraz… 0.600 0.300  1.10  -0.511  -1.20   0.0953
#>  9        9 Williams, 19… Willi… 1.00  0.600  1.60   0.     -0.511  0.470 
#> 10       10 Hammouda, 20… Hammo… 0.300 0.100  0.500 -1.20   -2.30  -0.693 
#> 11       11 Castellsague… Caste… 1.08  0.370  3.20   0.0770 -0.994  1.16  
#> 12       12 Castellsague… Caste… 0.340 0.0500 2.56  -1.08   -3.00   0.940 
#> 13       13 Castellsague… Caste… 0.870 0.340  2.23  -0.139  -1.08   0.802 
#> 14       14 Castellsague… Caste… 0.490 0.190  1.23  -0.713  -1.66   0.207 
#> 15       15 Castellsague… Caste… 0.240 0.0900 0.660 -1.43   -2.41  -0.416 
#> 16       16 Celentano, 1… Celen… 0.500 0.170  1.47  -0.693  -1.77   0.385 
#> # ... with 17 more variables: selnes <dbl>, group <fct>, case_num <dbl>,
#> #   control_num <dbl>, start_recruit <dbl>, stop_recruit <dbl>,
#> #   pub_year <dbl>, numpap <dbl>, ses <dbl>, gravidity <dbl>,
#> #   lifetimepart <dbl>, coitarche <dbl>, hpvstatus <dbl>, smoking <dbl>,
#> #   location <chr>, aair <dbl>, hpvrate <dbl>

tidymeta includes broom methods for cleaning meta-analysis results, although it currently only supports the metafor package. The tidy() function in broom puts results into a tidy data frame.

library(metafor)
#> Warning: package 'Matrix' was built under R version 3.4.4
meta4 <- rma(yi = lnes, sei = selnes, data = iud_cxca)
tidy(meta4) %>% 
  as_tibble() # for space
#> # A tibble: 17 x 8
#>    study   type   estimate std.error statistic  p.value conf.low conf.high
#>    <chr>   <chr>     <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#>  1 1       study   -0.511     0.354     -1.44  NA         -1.20     0.182 
#>  2 2       study   -0.223     0.223     -0.999 NA         -0.661    0.215 
#>  3 3       study   -0.117     0.0999    -1.17  NA         -0.312    0.0793
#>  4 4       study   -0.693     0.257     -2.70  NA         -1.20    -0.190 
#>  5 5       study   -0.462     0.262     -1.77  NA         -0.975    0.0509
#>  6 6       study   -0.799     0.205     -3.90  NA         -1.20    -0.397 
#>  7 7       study   -0.371     0.150     -2.47  NA         -0.665   -0.0772
#>  8 8       study   -0.511     0.331     -1.54  NA         -1.16     0.139 
#>  9 9       study    0.        0.250      0.    NA         -0.490    0.490 
#> 10 10      study   -1.20      0.411     -2.93  NA         -2.01    -0.399 
#> 11 11      study    0.0770    0.550      0.140 NA         -1.00     1.16  
#> 12 12      study   -1.08      1.00      -1.07  NA         -3.05     0.889 
#> 13 13      study   -0.139     0.480     -0.290 NA         -1.08     0.801 
#> 14 14      study   -0.713     0.476     -1.50  NA         -1.65     0.221 
#> 15 15      study   -1.43      0.508     -2.81  NA         -2.42    -0.431 
#> 16 16      study   -0.693     0.550     -1.26  NA         -1.77     0.385 
#> 17 Overall summa…  -0.449     0.0941    -4.77   1.83e-6   -0.634   -0.265

tidymeta also includes wrapper functions for working with meta-analysis packages in the context of the tidyverse. The main function for this is meta_analysis(), which models and tidies the object, as well as storing the results in the meta column to facilitate other analysis.

#  same as above but stores the meta-analysis object
iud_cxca %>% 
  meta_analysis(yi = lnes, sei = selnes, slab = study_name)
#> # A tibble: 17 x 10
#>    study    type  estimate std.error statistic  p.value conf.low conf.high
#>    <chr>    <chr>    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#>  1 Roura, … study  -0.511     0.354     -1.44  NA         -1.20     0.182 
#>  2 Lassise… study  -0.223     0.223     -0.999 NA         -0.661    0.215 
#>  3 Li, 2000 study  -0.117     0.0999    -1.17  NA         -0.312    0.0793
#>  4 Shields… study  -0.693     0.257     -2.70  NA         -1.20    -0.190 
#>  5 Castell… study  -0.462     0.262     -1.77  NA         -0.975    0.0509
#>  6 Castell… study  -0.799     0.205     -3.90  NA         -1.20    -0.397 
#>  7 Brinton… study  -0.371     0.150     -2.47  NA         -0.665   -0.0772
#>  8 Parazzi… study  -0.511     0.331     -1.54  NA         -1.16     0.139 
#>  9 William… study   0.        0.250      0.    NA         -0.490    0.490 
#> 10 Hammoud… study  -1.20      0.411     -2.93  NA         -2.01    -0.399 
#> 11 Castell… study   0.0770    0.550      0.140 NA         -1.00     1.16  
#> 12 Castell… study  -1.08      1.00      -1.07  NA         -3.05     0.889 
#> 13 Castell… study  -0.139     0.480     -0.290 NA         -1.08     0.801 
#> 14 Castell… study  -0.713     0.476     -1.50  NA         -1.65     0.221 
#> 15 Castell… study  -1.43      0.508     -2.81  NA         -2.42    -0.431 
#> 16 Celenta… study  -0.693     0.550     -1.26  NA         -1.77     0.385 
#> 17 Overall  summ…  -0.449     0.0941    -4.77   1.83e-6   -0.634   -0.265 
#> # ... with 2 more variables: meta <list>, weight <dbl>

tidymeta doesn’t actually do meta-analysis; it unifies existing tools with the tidyverse. The benefit of this approach is that you can do meta-analyses with tidy tools in mind. For example, if you want to conduct a sub-group analysis, you can use the group_by() function from dplyr. Here, group is a variable with information about study design.

ma <- iud_cxca %>% 
  group_by(group) %>% 
  meta_analysis(yi = lnes, sei = selnes, slab = study_name, exponentiate = TRUE)

ma
#> # A tibble: 21 x 11
#>    group    study     type  estimate std.error statistic  p.value conf.low
#>    <fct>    <chr>     <chr>    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>
#>  1 Nested … Roura, 2… study    0.600    0.354     -1.44  NA          0.300
#>  2 Nested … Subgroup… summ…    0.600    0.354     -1.44   0.149      0.300
#>  3 Populat… Lassise,… study    0.800    0.223     -0.999 NA          0.516
#>  4 Populat… Li, 2000  study    0.890    0.0999    -1.17  NA          0.732
#>  5 Populat… Shields,… study    0.500    0.257     -2.70  NA          0.302
#>  6 Populat… Castells… study    0.630    0.262     -1.77  NA          0.377
#>  7 Populat… Castells… study    0.450    0.205     -3.90  NA          0.301
#>  8 Populat… Subgroup… summ…    0.655    0.146     -2.90   0.00374    0.492
#>  9 Clinic-… Brinton,… study    0.690    0.150     -2.47  NA          0.514
#> 10 Clinic-… Parazzin… study    0.600    0.331     -1.54  NA          0.313
#> # ... with 11 more rows, and 3 more variables: conf.high <dbl>,
#> #   meta <list>, weight <dbl>

You can also do sensitivy analyses and cumulative analyses with sensitivity() and cumulative().

Visualization for Meta-Analysis

tidymeta includes functionality for working with results in ggplot2, including meta-analysis specific geoms (such as geom_funnel()) and quick plots for common visualizations.

With tidy data, most data visualizations for meta-analyses are easy to build from the ground up. Nevertheless, tidymeta has several quick plot functions to make the process easier. forest_plot() takes a tidied meta-analysis and plots the effect sizes.

fp <- ma %>% 
  forest_plot(group = group)

fp

Because the results are still ggplot2 objects, it’s easy to make changes to the plot to your liking.

fp <- fp + 
  scale_x_log() + 
  theme(axis.text.y = element_text(face = c("bold", rep("plain", 21))))

fp

tidymeta currently supports forest plots, funnel plots, influence plots, and cumulative plots.

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