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

Mean.MCI_AD, SD.MCI_AD, sm=“SMD”, data=data, studlab=paste(Author, Year))

## Warning in metacont(N.HC, Mean.HC, SD.HC, N.MCI_AD, Mean.MCI_AD, SD.MCI_AD, :

## Note, studies with non-positive values for n.e and / or n.c get no weight in

## meta-analysis.

meta$label.e <- “HC”

meta$label.c <- “MCI_AD”

print(meta, digits=2)

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

## Bjerke 2009 -0.60 [-1.13; -0.08] 5.4 9.0

## Mattsson 2009 -1.17 [-1.34; -0.99] 47.3 19.2

## Hertze 2010 -0.85 [-1.29; -0.41] 7.8 11.0

## Palmqvist 2012 NA 0.0 0.0

## Spencer 2019 NA 0.0 0.0

## Hansson 2006 NA 0.0 0.0

## Hansson 2007 NA 0.0 0.0

## Hampel 2004 -1.92 [-2.77; -1.07] 2.1 4.6

## Herukka 2007 -1.31 [-1.77; -0.84] 6.8 10.3

## Lanari 2009 NA 0.0 0.0

## Papaliagkas 2009 NA 0.0 0.0

## Eckerstrom 2010 -1.81 [-2.48; -1.14] 3.3 6.5

## Kester 2011 NA 0.0 0.0

## Seppala 2011 -0.76 [-1.47; -0.06] 3.0 6.1

## Buchhave 2012 -1.15 [-1.57; -0.73] 8.5 11.5

## Parnetti 2012 NA 0.0 0.0

## Prestia 2013 NA 0.0 0.0

## Leuzy 2015 NA 0.0 0.0

## Baldeiras 2018 -1.36 [-1.74; -0.99] 10.6 12.8

## Khoonsari 2019 -0.72 [-1.26; -0.19] 5.2 8.8

## Santangelo 2020 NA 0.0 0.0

## Eckerstrom 2020 NA 0.0 0.0

## Brys 2009 NA 0.0 0.0

##

## Number of studies combined: k = 10

## Number of observations: o = 1758

##

## SMD 95%-CI z p-value

## Fixed effect model -1.14 [-1.26; -1.02] -18.35 < 0.0001

## Random effects model -1.13 [-1.33; -0.93] -10.88 < 0.0001

##

## Quantifying heterogeneity:

## tau^2 = 0.0481; tau = 0.2192; I^2 = 50.5% [0.0%; 76.0%]; H = 1.42

[1.00; 2.04]

##

## Test of heterogeneity:

## Q d.f. p-value

## 18.18 9 0.0331

##

## Details on meta-analytical method:

## - Inverse variance method

## - DerSimonian-Laird estimator for tau^2

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