meta <- metacont(N.MCI_St, Mean.MCI_St, SD.MCI_St, N.MCI_AD, Mean.MCI_AD, SD.MCI_AD,

sm=“SMD”, data=data, studlab=paste(Author, Year))

meta$label.e <- “MCI_St”

meta$label.c <- “MCI_AD”

print(meta, digits=2)

## SMD 95%-CI %W(fixed) %W(random)

## Bjerke 2009 1.70 [ 1.11; 2.28] 2.1 4.3

## Mattsson 2009 0.54 [ 0.38; 0.69] 30.4 5.7

## Hertze 2010 0.90 [ 0.53; 1.26] 5.5 5.1

## Palmqvist 2012 1.46 [ 1.04; 1.87] 4.2 4.9

## Spencer 2019 0.76 [ 0.45; 1.06] 8.0 5.3

## Hansson 2006 1.50 [ 1.08; 1.92] 4.2 4.9

## Hansson 2007 1.03 [ 0.63; 1.43] 4.6 5.0

## Hampel 2004 0.79 [ 0.22; 1.36] 2.3 4.3

## Herukka 2007 0.85 [ 0.38; 1.32] 3.4 4.7

## Lanari 2009 1.30 [ 0.66; 1.94] 1.8 4.1

## Papaliagkas 2009 1.09 [ 0.15; 2.04] 0.8 3.0

## Eckerstrom 2010 1.26 [ 0.50; 2.02] 1.3 3.6

## Kester 2011 0.93 [ 0.51; 1.35] 4.2 4.9

## Seppala 2011 0.81 [ 0.21; 1.41] 2.0 4.2

## Buchhave 2012 1.93 [ 1.47; 2.39] 3.4 4.8

## Parnetti 2012 3.46 [ 2.79; 4.14] 1.6 3.9

## Prestia 2013 1.14 [ 0.49; 1.79] 1.7 4.0

## Leuzy 2015 0.70 [-0.03; 1.44] 1.4 3.7

## Baldeiras 2018 1.21 [ 0.85; 1.56] 5.8 5.2

## Khoonsari 2019 1.64 [ 1.07; 2.21] 2.2 4.3

## Santangelo 2020 0.78 [ 0.32; 1.25] 3.4 4.7

## Eckerstrom 2020 0.94 [ 0.58; 1.30] 5.7 5.2

##

## Number of studies combined: k = 22

## Number of observations: o = 2581

##

## SMD 95%-CI z p-value

## Fixed effect model 0.97 [0.88; 1.05] 22.18 < 0.0001

## Random effects model 1.19 [0.96; 1.42] 10.10 < 0.0001

##

## Quantifying heterogeneity:

## tau^2 = 0.2360; tau = 0.4858; I^2 = 83.8% [76.6%; 88.8%]; H = 2.48 [2.07; 2.98]

##

## Test of heterogeneity:

## Q d.f. p-value

## 129.49 21 < 0.0001

## ##Details on meta-analytical method:

## - Inverse variance method

## - DerSimonian-Laird estimator for tau^2

## - Hedges’ g (bias corrected standardised mean difference)