Metanalysis of prevalence of tribal tobacco use

library(readxl)

## Warning: package 'readxl' was built under R version 4.2.3

Metanalysis_Tobacco <- read_excel("~/work/My_reading_material/statistics_research/R_training/Metanalysis_6/Metanalysis_Tobacco.xlsx")
View(Metanalysis_Tobacco)
attach(Metanalysis_Tobacco)

Packages required

library(meta)

## Loading 'meta' package (version 6.0-0).
## Type 'help(meta)' for a brief overview.
## Readers of 'Meta-Analysis with R (Use R!)' should install
## older version of 'meta' package: https://tinyurl.com/dt4y5drs

library(tidyverse)

## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──

## ✔ ggplot2 3.3.6      ✔ purrr   0.3.5 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

metanalysis of proportion

meta_tobacco_tribal <- metaprop(event = `Tobacco use`,
                   n = `Sample Size`,
                   studlab = Author,
                   data = Metanalysis_Tobacco,
                   method = "GLMM",
                   sm = "PLOGIT",
                   fixed = FALSE,
                   random = TRUE,
                   hakn = TRUE,
                   title = "Tobacco use in Tribal population")

## Warning: Extra argument ('fixed') disregarded.

## Warning: Extra argument ('fixed') disregarded.

summary(meta_tobacco_tribal)

## Review:     Tobacco use in Tribal population
## 
##                         proportion           95%-CI
## Agarwal et al. 2023         0.3220 [0.3139; 0.3302]
## Ganie et al.                0.1767 [0.1677; 0.1860]
## Giri et al. 2022            0.7320 [0.7005; 0.7618]
## Gopalankutty et al 2020     0.7306 [0.6816; 0.7757]
## Kumar et al.2019            0.4500 [0.4005; 0.5002]
## Kumar et al.2022            0.9217 [0.8972; 0.9419]
## Haque et al.2021            0.5534 [0.4522; 0.6514]
## Kumar et al. 2022           0.5801 [0.5632; 0.5969]
## Kumar et al. 2022           0.6984 [0.6377; 0.7544]
## Mohandas 2019               0.5336 [0.4658; 0.6005]
## Murmu et al. 2023           0.4600 [0.4508; 0.4692]
## Muthanandam et al.2021      0.6144 [0.5324; 0.6919]
## Paul et al. 2018            0.6346 [0.5774; 0.6891]
## Placek et al. 2020          0.3913 [0.2912; 0.4986]
## Rao et al. 2018             0.6580 [0.6256; 0.6893]
## Thakur et al. 2019          0.6319 [0.5786; 0.6829]
## Tushi et al. 2018           0.7373 [0.6951; 0.7765]
## Chellappa et al. 2021       0.6463 [0.5680; 0.7193]
## Neelamana et al. 2020       0.6929 [0.6352; 0.7464]
## Sajeeva and Somanb 2018     0.8154 [0.7666; 0.8578]
## 
## Number of studies combined: k = 20
## Number of observations: o = 40133
## Number of events: e = 16480
## 
##                      proportion           95%-CI
## Random effects model     0.6108 [0.5156; 0.6983]
## 
## Quantifying heterogeneity:
##  tau^2 = 0.6729; tau = 0.8203; I^2 = 99.5% [99.5%; 99.6%]; H = 14.44 [13.56; 15.38]
## 
## Test of heterogeneity:
##        Q d.f. p-value             Test
##  3963.05   19       0        Wald-type
##  4882.31   19       0 Likelihood-Ratio
## 
## Details on meta-analytical method:
## - Random intercept logistic regression model
## - Maximum-likelihood estimator for tau^2
## - Hartung-Knapp (HK) adjustment for random effects model (df = 19)
## - Logit transformation
## - Clopper-Pearson confidence interval for individual studies

forest plot for the above metanalysis

forest.meta(meta_tobacco_tribal, layout = "RevMan5")

PNG

png("forest_plot.png", width = 800, height = 600, units = "px", res = 300)
forest.meta(meta_tobacco_tribal, layout = "RevMan5")
dev.off()

## png 
##   2