Exercise for depression yet again: Dauwan et al 2021
24 December, 2021
Init
library(kirkegaard)## Loading required package: tidyverse## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Loading required package: weights## Loading required package: Hmisc## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units## Loading required package: assertthat##
## Attaching package: 'assertthat'## The following object is masked from 'package:tibble':
##
## has_name## Loading required package: magrittr##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extract## Loading required package: psych##
## Attaching package: 'psych'## The following object is masked from 'package:Hmisc':
##
## describe## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## Loading required package: metafor## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack##
## Loading the 'metafor' package (version 3.0-2). For an
## introduction to the package please type: help(metafor)## Loading required package: rlang##
## Attaching package: 'rlang'## The following object is masked from 'package:magrittr':
##
## set_names## The following object is masked from 'package:assertthat':
##
## has_name## The following objects are masked from 'package:purrr':
##
## %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
## flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
## splice##
## Attaching package: 'kirkegaard'## The following object is masked from 'package:rlang':
##
## is_logical## The following object is masked from 'package:psych':
##
## rescale## The following object is masked from 'package:assertthat':
##
## are_equal## The following objects are masked from 'package:purrr':
##
## is_logical, is_numeric## The following object is masked from 'package:base':
##
## +load_packages(
readxl,
metafor,
metaviz
)
options(
digits = 3
)
theme_set(theme_bw())Data
- Dauwan et al 2021 https://link.springer.com/article/10.1007/s00415-019-09493-9#Fun
- Supplementary figure 3: results of depressive symptoms https://static-content.springer.com/esm/art%3A10.1007%2Fs00415-019-09493-9/MediaObjects/415_2019_9493_MOESM13_ESM.pdf
#read the data
d = readxl::read_excel("data/data.xlsx", range = "A1:F61") %>%
df_legalize_names()
#get the standard error from the CI
d %<>% mutate(
lower_95 = str_match(x95pct_CI_per_study, "(.+) to (.+)")[, 2] %>% as.numeric(),
upper_95 = str_match(x95pct_CI_per_study, "(.+) to (.+)")[, 3] %>% as.numeric(),
lower_CI_width = abs(Hedges_g - lower_95),
upper_CI_width = abs(Hedges_g - upper_95),
CI_width_mean = rowMeans(cbind(lower_CI_width, upper_CI_width)),
SE = CI_width_mean / 1.96 #95%
)
#arrange by SE
d = arrange(d, SE)Analysis
#plot p values
d %>%
ggplot(aes(p_value)) +
geom_histogram(binwidth = .05, boundary = 0)
#the critical region
d %>%
filter(p_value < .10) %>%
ggplot(aes(p_value)) +
geom_histogram(binwidth = .01, boundary = 0)
#RMA
rma_fit = d %>% arrange(SE) %$% rma(Hedges_g, sei = SE)
rma_fit##
## Random-Effects Model (k = 60; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.8565 (SE = 0.1834)
## tau (square root of estimated tau^2 value): 0.9254
## I^2 (total heterogeneity / total variability): 90.19%
## H^2 (total variability / sampling variability): 10.19
##
## Test for Heterogeneity:
## Q(df = 59) = 367.9339, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.8223 0.1292 6.3647 <.0001 0.5691 1.0755 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#funnel
rma_fit %>% GG_funnel()
GG_save("figs/funnel.png")
#forest
rma_fit %>% GG_forest(.alphabetic_sort_names = T)
#not in right order, fail
#with metaviz
metaviz::viz_forest(rma_fit)
GG_save("figs/forrest_better.png")
#funnel
metaviz::funnelinf(rma_fit, n = 1)
GG_save("figs/funnel_better.png")Meta
write_sessioninfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 19.3
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_DK.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] metaviz_0.3.1 readxl_1.3.1 kirkegaard_2021-12-14
## [4] rlang_0.4.12 metafor_3.0-2 Matrix_1.4-0
## [7] psych_2.1.9 magrittr_2.0.1 assertthat_0.2.1
## [10] weights_1.0.4 Hmisc_4.6-0 Formula_1.2-4
## [13] survival_3.2-13 lattice_0.20-45 forcats_0.5.1
## [16] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
## [19] readr_2.1.0 tidyr_1.1.4 tibble_3.1.6
## [22] ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-152 fs_1.5.0 lubridate_1.8.0
## [4] RColorBrewer_1.1-2 httr_1.4.2 tools_4.1.2
## [7] backports_1.3.0 bslib_0.3.1 utf8_1.2.2
## [10] R6_2.5.1 rpart_4.1-15 DBI_1.1.1
## [13] colorspace_2.0-2 nnet_7.3-16 withr_2.4.2
## [16] mnormt_2.0.2 tidyselect_1.1.1 gridExtra_2.3
## [19] rematch_1.0.1 compiler_4.1.2 cli_3.1.0
## [22] rvest_1.0.2 htmlTable_2.3.0 mice_3.13.0
## [25] xml2_1.3.2 labeling_0.4.2 sass_0.4.0
## [28] scales_1.1.1 checkmate_2.0.0 digest_0.6.28
## [31] minqa_1.2.4 foreign_0.8-81 rmarkdown_2.11
## [34] base64enc_0.1-3 jpeg_0.1-9 pkgconfig_2.0.3
## [37] htmltools_0.5.2 lme4_1.1-27.1 highr_0.9
## [40] dbplyr_2.1.1 fastmap_1.1.0 htmlwidgets_1.5.4
## [43] rstudioapi_0.13 farver_2.1.0 jquerylib_0.1.4
## [46] generics_0.1.1 jsonlite_1.7.2 gtools_3.9.2
## [49] Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
## [52] lifecycle_1.0.1 stringi_1.7.5 yaml_2.2.1
## [55] mathjaxr_1.4-0 MASS_7.3-54 grid_4.1.2
## [58] parallel_4.1.2 gdata_2.18.0 crayon_1.4.2
## [61] haven_2.4.3 splines_4.1.2 hms_1.1.1
## [64] tmvnsim_1.0-2 knitr_1.36 pillar_1.6.4
## [67] boot_1.3-28 reprex_2.0.1 glue_1.5.0
## [70] evaluate_0.14 latticeExtra_0.6-29 data.table_1.14.2
## [73] modelr_0.1.8 nloptr_1.2.2.3 png_0.1-7
## [76] vctrs_0.3.8 tzdb_0.2.0 cellranger_1.1.0
## [79] gtable_0.3.0 xfun_0.28 broom_0.7.10
## [82] cluster_2.1.2 ellipsis_0.3.2