Load data
# Load and display the data
rawdata<-read.csv("./DataMD.csv", header=TRUE)
rawdata
## Study Me Se Mc Sc Ne Nc
## 1 Asker (2015) 1.41 0.49 1.12 0.72 61 36
## 2 Karamanli (2017) 1.71 0.21 2.19 0.36 68 30
## 3 Xu (2017) 1.71 0.21 2.19 0.36 33 30
## 4 Cakir (2018) 2.00 0.12 2.04 0.19 70 30
attach(rawdata)
str(rawdata)
## 'data.frame': 4 obs. of 7 variables:
## $ Study: chr "Asker (2015)" "Karamanli (2017)" "Xu (2017)" "Cakir (2018)"
## $ Me : num 1.41 1.71 1.71 2
## $ Se : num 0.49 0.21 0.21 0.12
## $ Mc : num 1.12 2.19 2.19 2.04
## $ Sc : num 0.72 0.36 0.36 0.19
## $ Ne : int 61 68 33 70
## $ Nc : int 36 30 30 30
#Fixed Effect
rm.raw <- metacont(Ne,
Me,
Se,
Nc,
Mc,
Sc,
data=rawdata,
studlab=paste(Study),
comb.fixed = TRUE,
comb.random = FALSE,
prediction=TRUE,
sm="SMD")
rm.raw
## Number of studies combined: k = 4
## Number of observations: o = 358
##
## SMD 95%-CI z p-value
## Common effect model -0.5941 [-0.8287; -0.3595] -4.96 < 0.0001
## Prediction interval [-5.9883; 4.4031]
##
## Quantifying heterogeneity:
## tau^2 = 1.1544 [0.3300; 16.7708]; tau = 1.0744 [0.5744; 4.0952]
## I^2 = 95.2% [90.7%; 97.5%]; H = 4.58 [3.28; 6.38]
##
## Test of heterogeneity:
## Q d.f. p-value
## 62.80 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-Profile method for confidence interval of tau^2 and tau
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
#Random Effect
rm.raw <- metacont(Ne,
Me,
Se,
Nc,
Mc,
Sc,
data=rawdata,
studlab=paste(Study),
comb.fixed = FALSE,
comb.random = TRUE,
hakn = FALSE,
prediction=TRUE,
sm="SMD")
rm.raw
## Number of studies combined: k = 4
## Number of observations: o = 358
##
## SMD 95%-CI z p-value
## Random effects model -0.7926 [-1.8730; 0.2878] -1.44 0.1505
## Prediction interval [-5.9883; 4.4031]
##
## Quantifying heterogeneity:
## tau^2 = 1.1544 [0.3300; 16.7708]; tau = 1.0744 [0.5744; 4.0952]
## I^2 = 95.2% [90.7%; 97.5%]; H = 4.58 [3.28; 6.38]
##
## Test of heterogeneity:
## Q d.f. p-value
## 62.80 3 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-Profile method for confidence interval of tau^2 and tau
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(rm.raw, prediction = FALSE, lab.e = "OSA",
rightlabs = c("g","95% CI","weight"),
leftlabs = c("Study", "N","Mean","SD","N","Mean","SD"), text.random = "Overall effect")

#Random Effect
rm1.raw <- metacont(Ne,
Me,
Se,
Nc,
Mc,
Sc,
data=rawdata,
exclude = c(1),
studlab=paste(Study),
comb.fixed = FALSE,
comb.random = TRUE,
hakn = FALSE,
prediction=TRUE,
sm="SMD")
rm1.raw
## Number of studies combined: k = 3
## Number of observations: o = 358
##
## SMD 95%-CI z p-value
## Random effects model -1.2232 [ -2.1822; -0.2643] -2.50 0.0124
## Prediction interval [-13.2202; 10.7737]
##
## Quantifying heterogeneity:
## tau^2 = 0.6521 [0.1332; 27.5242]; tau = 0.8075 [0.3649; 5.2463]
## I^2 = 92.0% [79.7%; 96.8%]; H = 3.53 [2.22; 5.62]
##
## Test of heterogeneity:
## Q d.f. p-value
## 24.95 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-Profile method for confidence interval of tau^2 and tau
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)
forest(rm1.raw, prediction = FALSE, lab.e = "OSA",
rightlabs = c("g","95% CI","weight"),
leftlabs = c("Study", "N","Mean","SD","N","Mean","SD"), text.random = "Overall effect")
